Rozprawy doktorskie: „COMPLES NETWORK”

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HANOT, RAHUL. "COMMUNITY DTECTION USING FIRE PROPAGATION AND BOUNDARY VERTICES ALGORITHMS". Thesis, DELHI TECHNOLOGICAL UNIVERSITY, 2020. http://dspace.dtu.ac.in:8080/jspui/handle/repository/18779.

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Community detection in complex networks deal with grouping related nodes together and plays a vital role to understand the functioning of the system in real-life situations. Community detection is classified as an NP-hard problem. Various algorithms are currently available for it but the problem with these existing algorithms is either they have high in time complexity or they have not able to partition the network perfectly. In this paper, we propose a novel community detection algorithm that works in two phases. In the first phase, we apply fire propagation technique in which choosing an arbitrary vertex as the core vertex and connecting an adjacent vertex to it and shapes a community this is similar to how fire spreads in real-life situations. In the second phase,we use the result of the first phase of an overlapped community and detect all boundary vertices which are belongings to more than one communities and assign them to the single community based on the weight that each core vertex assign to that particular boundary vertex using Dijkstra distance and the count of the adjacent vertex that belong that community. The proposed algorithm performs well as compared to label propagation and walk-trap algorithm in terms of modularity score using various synthetic and real-world datasets.

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Kleineberg, Kaj Kolja. "Evolution and ecology of the digital world: A complex systems perspective". Doctoral thesis, Universitat de Barcelona, 2016. http://hdl.handle.net/10803/400404.

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Online social networks (OSNs) enable researchers to study the social universe at a previously unattainable scale. The worldwide impact and the necessity to sustain their rapid growth emphasize the importance to unravel the laws governing their evolution. We present a quantitative two-parameter model which reproduces the entire topological evolution of a quasi-isolated OSN with unprecedented precision from the birth of the network. This allows us to precisely gauge the fundamental macroscopic and microscopic mechanisms involved. Our findings suggest that the coupling between the real pre-existing underlying social structure, a viral spreading mechanism, and mass media influence govern the evolution of OSNs. The empirical validation of our model, on a macroscopic scale, reveals that virality is four to five times stronger than mass media influence and, on a microscopic scale, individuals have a higher subscription probability if invited by weaker social contacts, in agreement with the "strength of weak ties" paradigm. The simultaneous existence of numerous digital services naturally raises the question under which conditions these services can coexist. In analogy to population dynamics, the digital world is forming a complex ecosystem of interacting networks whose fitnesses depend on their ability to attract and maintain users' attention, which constitutes a limited resource. We introduce an ecological theory of the digital world which exhibits a stable coexistence of several networks as well as the domination of a single one, in contrast to the principle of competitive exclusion. Interestingly, our model also predicts that the most probable outcome is the coexistence of a moderate number of services, in agreement with empirical observations. In addition, we discuss the impact of heterogeneity in network fitnesses induced by competition between an international network, such as Facebook, and local services. To this end, we construct a 1:1000 scale model of the digital world, consisting of the 80 countries with the most Internet users. We show how inter-country social ties induce increased fitness of the international network. Under certain conditions, this leads to the extinction of local networks; whereas under different conditions, local networks can persist and even dominate the international network completely. Finally, we investigate how multiple coexisting networks, which form a so called multiplex system, facilitate search and navigation with only local knowledge. This task is especially important in decentralized architectures. In particular, we show that multiplex systems are not random combinations of single network layers. Instead, they are organized in specific ways dictated by hidden geometric correlations between the individual layers. We find that these correlations are strong in different real multiplexes, and form a key framework for answering many important questions. Specifically, we show that these geometric correlations facilitate: (i) the definition and detection of multidimensional communities, which are sets of nodes that are simultaneously similar in multiple layers; (ii) accurate trans-layer link prediction, where connections in one layer can be predicted by observing the hidden geometric space of another layer; and (iii) efficient targeted navigation in the multilayer system using only local knowledge, which outperforms navigation in the single layers only if the geometric correlations are sufficiently strong. Interestingly, many real systems fulfill these conditions. To conclude, from a system-level perspective, a prospering future in the digital age comprised of a diverse digital landscape with interacting, decentralized architectures is possible, but so is the opposite. It remains a task for society to create sufficient awareness and the correct incentives to create this future we desire.
Esta tesis está dedicada a los retos de un mundo interconectado que ha emergido a partir de la reciente revolución digital en el que servicios digitales se desarrollan y compiten en la ausencia de control central. Por tanto, herramientas, ideas y técnicas de análisis de sistemas complejos son útiles y especialmente adecuadas para describir la evolución y competencia entre redes sociales online. El éxito del Internet ha conectado individuos a escalas sin precedentes y la Web 2.0 promociona hoy en día colaboración global y el intercambio de ideas casi instantáneo. Sin embargo, la dominación de unos pocos poderosos monopolios de información representa un peligro para la libertad de ideas y decisiones de individuos. Por tanto, dos factores son esenciales para un futuro próspero en la era digital: diversidad digital y decentralización. En cuanto al primero, hemos introducido modelos basados en observaciones empíricas que permiten entender mejor la dinámica y las interacciones competitivas de las redes sociales online, los sistemas claves en el cosmos de la Web 2.0. En particular, nuestros descubrimientos revelan las condiciones en las cuales la diversidad digital se puede sostener. Con respecto al segundo, el diseño de arquitecturas descentralizadas contiene retos específicos, entre los que nos dirigimos especialmente a la búsqueda y navegación basada exclusivamente en conocimiento local. Hemos revelado en qué condiciones la existencia de muchas redes interaccionando facilita estas tareas. Afortunadamente, muchos sistemas reales cumplen estas condiciones. Para concluir, desde una perspectiva a nivel de sistema, un futuro próspero en el mundo digital compuesto por un paisaje digital diverso con arquitecturas descentralizadas en constante interacción es posible, pero no seguro. En esta situación, la conciencia, así como la creación de los incentivos adecuados, son retos importantes que nuestra sociedad debe afrontar. Crear conciencia suficiente e incentivos correctos para crear ese futuro sigue siendo un reto para la sociedad.

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Lordan, Oriol. "Airline route networks : a complex network approach". Doctoral thesis, Universitat Politècnica de Catalunya, 2014. http://hdl.handle.net/10803/144526.

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Communication via air routes is an important issue in a world organized around a web-like city network. In this context, the robustness of network infrastructures, e.g. air transport networks, are a central issue in transport geography. Disruption of communication links by intentional causes (e.g., terrorist attack on an airport) or unintentional (e.g., weather inclemency) could be a serious drawback for countries, regions and airlines. Policymakers and the management of airlines and alliances should be able to reduce the effects of such interruptions in order to ensure good communication through air transport (i.e., maximize the robustness of their network at a reasonable cost). The literature review of the study of air transport route networks through an analysis of complex networks has highlighted a lack of contributions to the study of the topology and the robustness of such networks, which contrasts with advances undertaken for other transport networks or communication systems. The literatura survey suggests areas in which research should be undertaken, based on the existing literature in other areas and from three different perspectives: global route networks, airline alliances and airlines. The aim of this research is to develop a better understanding of air traffic and, in particular, to be able to assess the potential damage of any airport being inoperative for a continent, country or airline.

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Paula, DemÃtrius Ribeiro de. "Dynamics of neural networks and cluster growth in complex networks". Universidade Federal do CearÃ, 2006. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=206.

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Conselho Nacional de Desenvolvimento CientÃfico e TecnolÃgico
Este dissertaÃÃo foi dividida em duas partes, na primeira parte nÃs propomos um modelo de crescimento competitivo de gregados em redes complexas para simular a propagaÃÃo de idÃias ou opiniÃes em comunidades. Investigamos como as distribuiÃÃes de tamanhos de agregados variam com a topologia de construÃÃo da rede e com o nÃmero de sementes aleatoriamente dispersas na estrutura. Para tal, analisamos redes do tipo de Erdos-RÃnyi, redes de contato preferencial e a chamada rede Apoloniana. Esta Ãltima apresenta distribuiÃÃes de tamanho de agregado em forma de uma lei de potÃncia com um expoente aproximadamente 1. Resultados similares sÃo observados com as distribuiÃÃes obtidas para as fraÃÃes de votos por candidato Ãs eleiÃÃes proporcionais para deputados no Brasil. Na segunda parte, analisamos o comportamento temporal da atividade neural em redes com caracterÃsticas de mundo pequeno e em redes construÃdas segundo o modelo do contato preferencial. Nesta primeira topologia, estudamos como a sÃrie temporal se comporta com a variaÃÃo do alcance das conexÃes. Em ambas as topologias, observamos a formaÃÃo de perÃodos e investigamos como estes variam com o tamanho da rede.
The process by which news trends and ideas propagate in social communities can have a profound impact in the life of individuals. To understand thi process, we introduce a competitive cluster growth model in complex networks. In our model, each cluster represents the set of individuals with a certain opinion or preference. We investigate how the cluster size distribution depends on the topology of the network and how it is affected by the number of initial seeds dispersed in the structure. We study our model using different network models, namely, the Erdos-Renyi geometry, the preferential attachment model, and the so-called Apollonian network. This last complex geometry displays a cluster size distribution that follows a power-law with an exponent 1.0. Similar results have been obtained for the distributions of number of votes per candidate in the proportional elections for federal representation in Brazil. In the second part of this work, we investigate the temporal behavior of neural networks with small world topology and in networks built according to the preferential attachment model. In the first case we study the effect of the range of connections on the behavior of the time series. In both topologies, we detect the existence of cycles and investigate how their periods depend on the size of the system.

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Reis, Saulo-Davi Soares e. "Navegação em redes espacialmente correlacionadas". reponame:Repositório Institucional da UFC, 2009. http://www.repositorio.ufc.br/handle/riufc/12888.

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REIS, Saulo Davi Soares e. Navegação em redes espacialmente correlacionadas. 2009. 72 f. Dissertação (Mestrado em Física) - Programa de Pós-Graduação em Física, Departamento de Física, Centro de Ciências, Universidade Federal do Ceará, Fortaleza, 2009.
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A significant number of real networks have well-defined spatial characteristics. We studied how network with spatially correlated topolgies can influence the processes of navigation through them. For this, we study the behavior of the average shortest-path length to networks defined within Kleinberg’s model [1, 2] to analyze the navigation dictated by rules of global knowledge. The Kleinberg’s model is characterized by allowing long-range connections between two vertices u and v distributed by a power-law probability distribution. For a better understanding of the topological characteristics presented by this family of networks, we applied the epidemic model susceptible-infected-susceptible (SIS) and we found that, we see that the Kleinberg’s model presents the small-world phenomenon only for a certain range of values of the clustering exponent α. We introduced a model of spatially embedded networks, conceptually based on the Kleinberg’s model. This model consist in introduction of a constrain to the distribution of long-range connections. We associate his constrain to a possible cost involved in the process of adding new long-range connections to the network. We studied how this cost constrain affects the navigation through the system, taking as a basis for comparison the work of Kleinberg for navigation with local knowledge, and our results conserning for navigation with global knowledge.
Um número significativo de redes reais apresentam características espaciais bem definidas. Nós estudamos como topologias de redes espacialmente correlacionadas podem influenciar processos de navegação através das mesmas. Para isso estudamos o comportamento do mínimo caminho médio para redes definidas dentro de modelo de Kleinberg para analisar a navegação ditada por regras de conhecimento global. O modelo que Kleinberg caracteriza-se por permitir conexões de longo alcance entre dois vértices u e v distribuídas por uma distribuição de probabilidade em lei de potência. Para um melhor entendimento das características topológicas apresentadas por essa família de redes, nós aplicamos o modelo epidêmico suscetível-infectado-suscetível (SIS), e com isso verificamos que o modelo de Kleinberg apresenta fenômeno de mundo pequeno apenas para uma determinada faixa de valores assumidos pelo expoente de agregação α. Em seguida, introduzimos um modelo de redes espacialmente embutidas, conceitualmente inspirado no modelo de Kleinberg. Este traduz-se na introdução de um vínculo para a distribuição das conexões de longo alcance. Associamos este vínculo a um possível custo envolvido no processo de adição de novas conexões de longo alcance à rede. Estudamos como esse vínculo no custo afeta a navegação na rede, tendo como base de comparação os trabalhos de Kleinberg para a navegação com conhecimento local da topologia, e nossos resultados considerando a navegação com conhecimento global.

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Khorramzadeh, Yasamin. "Network Reliability: Theory, Estimation, and Applications". Diss., Virginia Tech, 2015. http://hdl.handle.net/10919/64383.

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Network reliability is the probabilistic measure that determines whether a network remains functional when its elements fail at random. Definition of functionality varies depending on the problem of interest, thus network reliability has much potential as a unifying framework to study a broad range of problems arising in complex network contexts. However, since its introduction in the 1950's, network reliability has remained more of an interesting theoretical construct than a practical tool. In large part, this is due to well-established complexity costs for both its evaluation and approximation, which has led to the classification of network reliability as a NP-Hard problem. In this dissertation we present an algorithm to estimate network reliability and then utilize it to evaluate the reliability of large networks under various descriptions of functionality. The primary goal of this dissertation is to pose network reliability as a general scheme that provides a practical and efficiently computable observable to distinguish different networks. Employing this concept, we are able to demonstrate how local structural changes can impose global consequences. We further use network reliability to assess the most critical network entities which ensure a network's reliability. We investigate each of these aspects of reliability by demonstrating some example applications.
Ph. D.

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Reis, Elohim Fonseca dos 1984. "Criticality in neural networks = Criticalidade em redes neurais". [s.n.], 2015. http://repositorio.unicamp.br/jspui/handle/REPOSIP/276917.

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Orientadores: José Antônio Brum, Marcus Aloizio Martinez de Aguiar
Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Física Gleb Wataghin
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Resumo: Este trabalho é dividido em duas partes. Na primeira parte, uma rede de correlação é construída baseada em um modelo de Ising em diferentes temperaturas, crítica, subcrítica e supercrítica, usando um algorítimo de Metropolis Monte-Carlo com dinâmica de \textit{single-spin-flip}. Este modelo teórico é comparado com uma rede do cérebro construída a partir de correlações das séries temporais do sinal BOLD de fMRI de regiões do cérebro. Medidas de rede, como coeficiente de aglomeração, mínimo caminho médio e distribuição de grau são analisadas. As mesmas medidas de rede são calculadas para a rede obtida pelas correlações das séries temporais dos spins no modelo de Ising. Os resultados da rede cerebral são melhor explicados pelo modelo teórico na temperatura crítica, sugerindo aspectos de criticalidade na dinâmica cerebral. Na segunda parte, é estudada a dinâmica temporal da atividade de um população neural, ou seja, a atividade de células ganglionares da retina gravadas em uma matriz de multi-eletrodos. Vários estudos têm focado em descrever a atividade de redes neurais usando modelos de Ising com desordem, não dando atenção à estrutura dinâmica. Tratando o tempo como uma dimensão extra do sistema, a dinâmica temporal da atividade da população neural é modelada. O princípio de máxima entropia é usado para construir um modelo de Ising com interação entre pares das atividades de diferentes neurônios em tempos diferentes. O ajuste do modelo é feito com uma combinação de amostragem de Monte-Carlo e método do gradiente descendente. O sistema é caracterizado pelos parâmetros aprendidos, questões como balanço detalhado e reversibilidade temporal são analisadas e variáveis termodinâmicas, como o calor específico, podem ser calculadas para estudar aspectos de criticalidade
Abstract: This work is divided in two parts. In the first part, a correlation network is build based on an Ising model at different temperatures, critical, subcritical and supercritical, using a Metropolis Monte-Carlo algorithm with single-spin-flip dynamics. This theoretical model is compared with a brain network built from the correlations of BOLD fMRI temporal series of brain regions activity. Network measures, such as clustering coefficient, average shortest path length and degree distributions are analysed. The same network measures are calculated to the network obtained from the time series correlations of the spins in the Ising model. The results from the brain network are better explained by the theoretical model at the critical temperature, suggesting critical aspects in the brain dynamics. In the second part, the temporal dynamics of the activity of a neuron population, that is, the activity of retinal ganglion cells recorded in a multi-electrode array was studied. Many studies have focused on describing the activity of neural networks using disordered Ising models, with no regard to the dynamic nature. Treating time as an extra dimension of the system, the temporal dynamics of the activity of the neuron population is modeled. The maximum entropy principle approach is used to build an Ising model with pairwise interactions between the activities of different neurons at different times. Model fitting is performed by a combination of Metropolis Monte Carlo sampling with gradient descent methods. The system is characterized by the learned parameters, questions like detailed balance and time reversibility are analysed and thermodynamic variables, such as specific heat, can be calculated to study critical aspects
Mestrado
Física
Mestre em Física
2013/25361-6
FAPESP

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Jiang, Jian. "Modeling of complex network, application to road and cultural networks". Phd thesis, Université du Maine, 2011. http://tel.archives-ouvertes.fr/tel-00691129.

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Many complex systems arising from nature and human society can be described as complex networks. In this dissertation, on the basis of complex network theory, we pay attention to the topological structure of complex network and the dynamics on it. We established models to investigate the influences of the structure on the dynamics of networks and to shed light on some peculiar properties of complex systems. This dissertation includes four parts. In the first part, the empirical properties (degree distribution, clustering coefficient, diameter, and characteristic path length) of urban road network of Le Mans city in France are studied. The degree distribution shows a double power-law which we studied in detail. In the second part, we propose two models to investigate the possible mechanisms leading to the deviation from simple power law. In the first model, probabilistic addition of nodes and links, and rewiring of links are considered; in the second one, only random and preferential link growth is included. The simulation results of the modelling are compared with the real data. In the third part,the probabilistic uncertainty behavior of double power law distribution is investigated. The network optimization and optimal design of scale free network to random failures are discussed from the viewpoint of entropy maximization. We defined equilibrium network ensemble as stationary ensembles of graphs by using some thermodynamics like notions such as "energy", "temperature", "free energy" for network. In the forth part, an union-division model is established to investigate the time evolution of certain networks like cultural or economical networks. In this model, the nodes represent, for example, the cultures. Several quantities such as richness, age, identity, ingredient etc. are used to parameterize the probabilistic evolution of the network. The model offers a long term view on the apparently periodic dynamics of an ensemble of cultural or economic entities in interaction.

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Hollingshad, Nicholas W. "A Non-equilibrium Approach to Scale Free Networks". Thesis, University of North Texas, 2012. https://digital.library.unt.edu/ark:/67531/metadc149609/.

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Many processes and systems in nature and society can be characterized as large numbers of discrete elements that are (usually non-uniformly) interrelated. These networks were long thought to be random, but in the late 1990s, Barabási and Albert found that an underlying structure did in fact exist in many natural and technological networks that are now referred to as scale free. Since then, researchers have gained a much deeper understanding of this particular form of complexity, largely by combining graph theory, statistical physics, and advances in computing technology. This dissertation focuses on out-of-equilibrium dynamic processes as they unfold on these complex networks. Diffusion in networks of non-interacting nodes is shown to be temporally complex, while equilibrium is represented by a stable state with Poissonian fluctuations. Scale free networks achieve equilibrium very quickly compared to regular networks, and the most efficient are those with the lowest inverse power law exponent. Temporally complex diffusion also occurs in networks with interacting nodes under a cooperative decision-making model. At a critical value of the cooperation parameter, the most efficient scale free network achieves consensus almost as quickly as the equivalent all-to-all network. This finding suggests that the ubiquity of scale free networks in nature is due to Zipf's principle of least effort. It also suggests that an efficient scale free network structure may be optimal for real networks that require high connectivity but are hampered by high link costs.

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Rocha, Luis Enrique Correa da. "Redes acopladas: estrutura e dinâmica". Universidade de São Paulo, 2007. http://www.teses.usp.br/teses/disponiveis/76/76132/tde-11092007-183106/.

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A teoria das redes complexas tem se consolidado por seu forte caráter interdisciplinar, relativa simplicidade conceitual e ampla aplicabilidade na modelagem de sistemas reais. Embora tendo evoluído rapidamente, uma série de problemas ainda não foram estudados usando as redes complexas. Em especial, sistemas envolvendo acoplamento e interação entre diferentes redes complexas têm sido pouco investigados. Na presente monografia, apresentamos duas contribuições fundamentais no estudo desses sistemas. A primeira consiste num modelo que descreve a interação entre um padrão de massa evoluindo numa rede regular com uma rede complexa que se organiza para impedir a evolução desse padrão. Os vértices da rede complexa se ativam e se movem sobre a rede regular conforme são requisitados por seus vizinhos, que se ativam pela rede regular. Essa última ativação ocorre quando a concentração de massa ultrapassa um limiar na respectiva posição do vértice e consiste em liberar uma difusão oposta de massa neutralizadora contra a massa original. A dinâmica mostrou-se completamente relacionada à estrutura da rede de controle. A presença de concentradores no modelo de Barabási-Albert tem papel fundamental para acelerar o processo de geração de massa neutralizadora. Por outro lado, a distribuição uniforme de vizinhos da rede de Erdös-Rényi resultou numa melhora de desempenho na presença de várias regiões distintas contendo massa original. A segunda contribuição consiste num modelo de interação entre duas espécies (predador e presa) através de campos sensitivos, que dependem da distância Euclidiana entre dois indivíduos e do seu respectivo tipo. Padrões espaço-temporais emergem nesse sistema e estão diretamente relacionados à intensidade de atração entre os indivíduos da mesma espécie. Para entender a evolução do sistema e quantificar a transferência de informação entre os diferentes aglomerados, duas redes complexas são construídas onde os vértices representam os indivíduos. Na primeira rede, o peso das conexões é dado pela distância Euclidiana entre os indivíduos e na segunda, pelo tempo que eles permaneceram suficientemente próximos. A partir de um mecanismo de fusão entre as duas redes, obtemos uma terceira rede complexa onde os vértices correspondem a grupos espaciais definidos a partir de um processo de limiarização dos pesos da primeira rede. Algumas configurações de parâmetros privilegiam a sobrevivência de presas enquanto outras beneficiam a caça dos predadores.
Complex network theory has become very popular because of its interdisciplinarity, conceptual simplicity and wide applicability to model real systems. Although fast growing, there is a number of problems which have not been addressed by using complex networks. For example, few efforts have been directed to systems involving coupling and interaction between different complex networks. In the following monography, we present two fundamental contributions in the study of such systems. The first consists in a model which describes the interaction dynamics between a mass pattern evolving in a regular network with a complex network, which are expected to control the pattern evolution. As soon as a complex network node is activated by the regular network, it requests help from its topological neighbours and activates them. The activation is triggered when the mass concentration overcomes a threshold in the node position and consists in liberating an opposite diffusion intended to eliminate the original pattern. The dynamics is completely related to the structure of the control network. The existence of hubs in the Barabási-Albert model plays a fundamental role to accelerate the opposite mass generation. Conversely, the uniform distribution of neighbours in the Erdös-Rényi network provided an increase in the efficiency when several focuses of the original pattern were distributed in the regular network. The second contribution consists in a model based on interactions between two species (predator and prey) provided by sensitive fields which depends of the Euclidean distance between two agents and on their respective types. Spatio-temporal patterns emerge in the system which are directly related to the attraction intensity between same species agents. To understand the dynamics evolution and quantify the information transfer through different clusters, we built two complex networks where the nodes represent the agents. In the first network, the edge weight is given by the Euclidean distance between two agents and, in the second network, by the amount of time two agents become close one another. By following a merging process, another network is obtained whose nodes correspond to spatial groups defined by a weight thresholding process in the first network. Some configurations favor the preys survival, while predators efficiency are improved by other ones.

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Paula, Demétrius Ribeiro de. "Dinâmica de redes neurais e formação de agregados em redes complexas". reponame:Repositório Institucional da UFC, 2006. http://www.repositorio.ufc.br/handle/riufc/9649.

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PAULA, Demétrius Ribeiro de. Dinâmica de redes neurais e formação de agregados em redes complexas. 2006. 90 f. Dissertação (Mestrado em Física) - Programa de Pós-Graduação em Física, Departamento de Física, Centro de Ciências, Universidade Federal do Ceará, Fortaleza, 2006.
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The process by which news trends and ideas propagate in social communities can have a profound impact in the life of individuals. To understand thi process, we introduce a competitive cluster growth model in complex networks. In our model, each cluster represents the set of individuals with a certain opinion or preference. We investigate how the cluster size distribution depends on the topology of the network and how it is affected by the number of initial seeds dispersed in the structure. We study our model using different network models, namely, the Erdos-Renyi geometry, the preferential attachment model, and the so-called Apollonian network. This last complex geometry displays a cluster size distribution that follows a power-law with an exponent 1.0. Similar results have been obtained for the distributions of number of votes per candidate in the proportional elections for federal representation in Brazil. In the second part of this work, we investigate the temporal behavior of neural networks with small world topology and in networks built according to the preferential attachment model. In the first case we study the effect of the range of connections on the behavior of the time series. In both topologies, we detect the existence of cycles and investigate how their periods depend on the size of the system.
Este dissertação foi dividida em duas partes, na primeira parte nós propomos um modelo de crescimento competitivo de gregados em redes complexas para simular a propagação de idéias ou opiniões em comunidades. Investigamos como as distribuições de tamanhos de agregados variam com a topologia de construção da rede e com o número de sementes aleatoriamente dispersas na estrutura. Para tal, analisamos redes do tipo de Erdos-Rényi, redes de contato preferencial e a chamada rede Apoloniana. Esta última apresenta distribuições de tamanho de agregado em forma de uma lei de potência com um expoente aproximadamente 1. Resultados similares são observados com as distribuições obtidas para as frações de votos por candidato às eleições proporcionais para deputados no Brasil. Na segunda parte, analisamos o comportamento temporal da atividade neural em redes com características de mundo pequeno e em redes construídas segundo o modelo do contato preferencial. Nesta primeira topologia, estudamos como a série temporal se comporta com a variação do alcance das conexões. Em ambas as topologias, observamos a formação de períodos e investigamos como estes variam com o tamanho da rede.

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Sousa, Sandro Ferreira. "Estudo das propriedades e robustez da rede de transporte público de São Paulo". Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/100/100132/tde-29072016-103544/.

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Sistemas Complexos são característicos por possuir uma rede interna representando o relacionamento estrutural entre seus elementos e uma forma natural de interpretar essa interação é através de um grafo. Neste trabalho, o sistema de transporte público urbano de São Paulo é reinterpretado de forma acoplada (ônibus e metrô juntos) como uma rede complexa, abstraindo detalhes operacionais e focando na conectividade. Pelo grafo empiricamente gerado, é feita uma caraterização estatística nas métricas de redes complexas, onde diferentes valores de raio de distância são usados para agrupar pontos e estações próximas que antes se apresentavam desconectados. Esse agrupamento pode ser interpretado como uma ferramenta de política pública, representando a disposição do usuário em se locomover ao ponto mais próximo para acessar o transporte. O processo mostrou que aumentar essa disposição gera grande redução na distância e número de passos entre ônibus, trens e linhas de metrô para atingir todos os destinos da rede. É utilizado um modelo exploratório que testa a robustez da rede aleatoriamente, deterministicamente e probabilisticamente tendo como alvo pontos e linhas. De acordo com os raios de agrupamento, definido como disposição, diferentes valores de fragmentação foram obtidos diante dos ataques simulados. Esses resultados suportam duas principais características observadas na literatura de redes deste tipo: possuem um elevado grau de robustez à falhas aleatórias, mas são vulneráveis a ataques tendo como alvo nós ou links importantes
Complex systems are characteristic by having an internal network representing the structural relationship between its elements and a natural way to interpret this interaction is through a graph. In this work, the urban public transport system of São Paulo is reinterpreted as a coupled (bus and subway) complex network, bypassing operational details and focusing on connectivity. Using the empirically generated graph, a statistical characterisation is made by network metrics where different radius values are used to group nearby stops and stations that were disconnected before. That can be interpreted as a public policy tool, representing the user\'s willingness to get around the nearest point to access transportation. This process has shown that increasing this willingness generates great reduction in the distance and in the number of jumps between buses, trains and subways lines to achieve all the network destinations. An exploratory model is used to test the robustness of the network by randomly, deterministically and preferentially targeting the stops and service lines. According to the grouping radius, aka willingness, different fragmentation values were obtained under attack simulations. These findings support two main characteristics observed in such networks literature: they have a high degree of robustness to random failures, but are vulnerable to targeted attacks

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Antiqueira, Lucas. "Relações da estrutura de redes complexas com as dinâmicas do passeio aleatório, de transporte e de sincronização". Universidade de São Paulo, 2011. http://www.teses.usp.br/teses/disponiveis/76/76132/tde-13032012-100543/.

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O relacionamento entre estrutura e dinâmica em redes complexas foi considerado utilizando-se uma ampla gama de diferentes técnicas. Diversas redes reais foram estudadas em termos das correlações entre grau e atividade. A medida de atividade é definida como a proporção de visitas por vértice no regime estacionário do passeio aleatório simples. O estudo desse tipo de correlação é importante pois pode fornecer subsídios para que uma propriedade dinâmica de um vértice possa ser obtida somente analisando-se seu(s) grau(s). O conceito de acessibilidade foi abordado nesse contexto, permitindo que fossem evidenciadas diferentes correlações, em redes como a WWW, de acordo com a intensidade de acessibilidade dos vértices. Propôs-se também um novo modelo de rede baseado no crescimento do número de vértices em que novas conexões são criadas com probabilidade proporcional à atividade de cada vértice. Esse modelo pode ser entendido como uma generalização do modelo de Barabási e Albert para redes com arestas direcionadas. Utilizando-se um conjunto de diversas medidas estruturais, mostrou-se que o novo modelo apresenta, entre outros modelos tradicionais de redes, a maior compatibilidade com três redes corticais. Foi também desenvolvido um método para caracterização da distribuição de subgrafos e seus inter-relacionamentos. O principal aspecto dessa metodologia é a expansão gradual dos subgrafos, desenvolvida para que os vértices que encontram-se fora de subgrafos possam ter suas relevâncias quantificadas em termos da importância no estabelecimento das conexões entre subgrafos. Experimentos para ilustração do método foram realizados utilizando-se quatro modelos de redes e cinco redes reais, e os resultados obtidos foram relacionados aos processos dinâmicos de transporte e de espalhamento. Outro tópico aqui considerado é o dos efeitos da amostragem de redes corticais, quantificados por meio de análise multivariada e classificação, fazendo uso de um conjunto de medidas estruturais de redes. Esses efeitos também foram mensurados em termos do comportamento dinâmico das redes (sincronização e acessibilidade). Simulações dos métodos de encefalografia MEG e EEG mostraram que as redes amostradas podem apresentar características bem diferentes das da rede original, principalmente no caso de amostras pequenas. Adicionalmente, a rede integrada da bactéria Escherichia coli foi analisada, a qual incorpora (i) regulação de transcrição gênica, (ii) vias metabólicas e de sinalização e (iii) interações entre proteínas. Outliers foram identificados no relacionamento entre grau e atividade, os quais representam reguladores globais de transcrição. Além disso, verificou-se que esses outliers são genes altamente expressos em diferentes condições, apresentando, portanto, uma natureza global no controle de diversos outros genes da célula.
The relationship between structure and dynamics was addressed by employing a wide range of different approaches. First, the correlations between degree and activity were studied in various real-world networks. The activity is defined as the proportion of visits to each node in the steady-state regime of the simple random walk. This type of correlation can provide means to assess node activity only in terms of the degree. The concept of accessibility was included in this analysis, showing an intimate relationship (in networks such as the WWW) between the type of correlation and the level of accessibility observed on nodes. A new complex network model founded on growth was also proposed, with new connections being established proportionally to the current activity of each node. This model can be understood as a generalization of the Barabási-Albert model for directed networks. By using several topological measurements we showed that this new model provides, among several other traditional theoretical types of networks, the greatest compatibility with three real-world cortical networks. Additionally, we developed a novel approach considering non-overlapping subgraphs and their interrelationships and distribution through a given network. The main aspect of the methodology is a novel merging procedure developed to assess the relevance of nodes (in relation to the overall subgraph interconnectivity) lying outside subgraphs. Experiments were carried out on four types of network models and five instances of real-world networks, in order to illustrate the application of the method. Furthermore, these results were related to the properties of the transport and spreading processes. Other topic here addressed is the sampling problem in cortical networks. Effects of sampling were quantified using multivariate analysis and classifiers based on structural network measurements. Samples were also evaluated in terms of their dynamical behavior using a synchronization model and the measure of accessibility. By simulating MEG/EEG recordings it was found that sampled networks may substantially deviate from the respective original networks, mainly for small sample sizes. We also report an analysis of the integrated network of Escherichia coli, which incorporates (i) transcriptional regulatory interactions, (ii) metabolic/signaling feedback and (iii) protein-protein interactions. Network outliers, which represent global transcriptional regulators, were identified in the relationship between out-degree and activity. These outliers are highly and widely expressed across conditions, therefore supporting their global nature in controlling many genes in the cell.

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Haschke, Robert. "Bifurcations in discrete time neural networks : controlling complex network behaviour with inputs". kostenfrei, 2004. http://deposit.ddb.de/cgi-bin/dokserv?idn=973184663.

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Reis, Saulo Davi Soares e. "NavegaÃÃo em redes espacialmente correlacionadas". Universidade Federal do CearÃ, 2009. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=2817.

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Conselho Nacional de Desenvolvimento CientÃfico e TecnolÃgico
Um nÃmero significativo de redes reais apresentam caracterÃsticas espaciais bem definidas. NÃs estudamos como topologias de redes espacialmente correlacionadas podem influenciar processos de navegaÃÃo atravÃs das mesmas. Para isso estudamos o comportamento do mÃnimo caminho mÃdio para redes definidas dentro de modelo de Kleinberg para analisar a navegaÃÃo ditada por regras de conhecimento global. O modelo que Kleinberg caracteriza-se por permitir conexÃes de longo alcance entre dois vÃrtices u e v distribuÃdas por uma distribuiÃÃo de probabilidade em lei de potÃncia. Para um melhor entendimento das caracterÃsticas topolÃgicas apresentadas por essa famÃlia de redes, nÃs aplicamos o modelo epidÃmico suscetÃvel-infectado-suscetÃvel (SIS), e com isso verificamos que o modelo de Kleinberg apresenta fenÃmeno de mundo pequeno apenas para uma determinada faixa de valores assumidos pelo expoente de agregaÃÃo α. Em seguida, introduzimos um modelo de redes espacialmente embutidas, conceitualmente inspirado no modelo de Kleinberg. Este traduz-se na introduÃÃo de um vÃnculo para a distribuiÃÃo das conexÃes de longo alcance. Associamos este vÃnculo a um possÃvel custo envolvido no processo de adiÃÃo de novas conexÃes de longo alcance Ã rede. Estudamos como esse vÃnculo no custo afeta a navegaÃÃo na rede, tendo como base de comparaÃÃo os trabalhos de Kleinberg para a navegaÃÃo com conhecimento local da topologia, e nossos resultados considerando a navegaÃÃo com conhecimento global.
A significant number of real networks have well-defined spatial characteristics. We studied how network with spatially correlated topolgies can influence the processes of navigation through them. For this, we study the behavior of the average shortest-path length to networks defined within Kleinbergâs model [1, 2] to analyze the navigation dictated by rules of global knowledge. The Kleinbergâs model is characterized by allowing long-range connections between two vertices u and v distributed by a power-law probability distribution. For a better understanding of the topological characteristics presented by this family of networks, we applied the epidemic model susceptible-infected-susceptible (SIS) and we found that, we see that the Kleinbergâs model presents the small-world phenomenon only for a certain range of values of the clustering exponent α. We introduced a model of spatially embedded networks, conceptually based on the Kleinbergâs model. This model consist in introduction of a constrain to the distribution of long-range connections. We associate his constrain to a possible cost involved in the process of adding new long-range connections to the network. We studied how this cost constrain affects the navigation through the system, taking as a basis for comparison the work of Kleinberg for navigation with local knowledge, and our results conserning for navigation with global knowledge.

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Silva, Filipi Nascimento. "Redes complexas: novas metodologias e modelagem de aquisição de conhecimento". Universidade de São Paulo, 2009. http://www.teses.usp.br/teses/disponiveis/76/76132/tde-15032010-105321/.

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Estudos em redes complexas têm ganhado cada vez mais atenção devido ao seu potencial de representação simples de modelos complexos em diversas áreas de conhecimento. A obtenção de modelos quantitativos que representem fenômenos observados da natureza, assim como o desenvolvimento de metodologias de caracterização de redes complexas, tornaram-se essenciais para a compreensão e desenvolvimento de pesquisas com essas estruturas. Este trabalho tem como objetivo desenvolver e estudar alguns métodos recentes, usados para a caracterização de redes complexas, explorando-os no contexto da modelagem de conhecimento. Para isso, duas redes complexas foram geradas, uma rede de colaboração de pesquisadores da USP e outra obtida a partir do banco de dados de artigos da Wikipédia, considerando apenas aqueles da categoria de teoremas matemáticos. As medidas concêntricas, que foram recentemente formalizadas, são exploradas e aplicadas às redes descritas, assim como para diversos modelos teóricos, fornecendo informações muito relevantes sobre a topologia dessas redes. Resultados ainda mais interessantes são obtidos pela caracterização dos vértices da rede de colaboração, que revelam padrões de interdisciplinaridade entre as diferentes áreas do conhecimento. Um modelo de aquisição de conhecimento também foi proposto, aplicando a utilização de simulações de múltiplos agentes interagentes que caminham por uma rede complexa segundo uma heurística auto-esquivante. Resultados dessas simulações, realizadas para a rede da Wikipédia e outros modelos teóricos, mostram que certas configurações de parâmetros e de redes apresentam melhor desempenho na aquisição do conhecimento, com a rede de teoremas apresentando o pior deles. Entretanto, diferentemente do que era esperado, a variação da memória dos agentes pouco influência a velocidade de aquisição de conhecimento dos agentes. A freqüência de acesso dos vértices pelos agentes também foi determinada e explorada superficialmente. Diversos softwares foram desenvolvidos para uso neste projeto de mestrado, dentre eles destaca-se o visualizador 3D, que se tornou indispensável para a análise das contribuições das outras propriedades apresentadas.
Studies of complex networks have gained increasing research interest in recent years, in part due to its potential for simple representation of complex systems in various fields of science. The needs of quantitative models representing observed phenomena, as well the development of methods for the characterization of complex networks, is a essential matter for the development and understanding of scientific researches exploring such structures. This work aims to develop and study some new methods for the characterization of complex networks, exploring them in the context of knowledge modeling. Initially, two complex networks were developed, a collaborative network of researchers from the Universidade de Sao Paulo and the other obtained from the database of Wikip´edia articles, considering only those strict related to mathematical theorems. The recently formalized concentric measurements are explored and applied to the described networks, as well to other several theoretical models, providing much more information about the topology of these networks than by the use of traditional measurements. Even more interesting results are obtained by the characterization of the vertices of the collaboration network, which reveal patterns of interdisciplinarity among the many fields of science. A model of knowledge acquisition has also been proposed by the use of simulations of multiple interacting agents walking through a complex network in self-avoiding trajectories. Results of those simulations, performed for the network of Wikipedia and other theoretical models shows that certain sets of parameters and networks perform better in the acquisition of knowledge, through the network of theorems presenting the worst of them. However, unlike what should be expected on the basis of intuition, the agents memories do not play much influence to the speed of acquisition of knowledge. The agent access frequencies of vertices was also been obtained and explored superficially in order to determine where the agents walk more ofen. Several softwares had been developed in this masters thesis project, among these, there is a complex network computational visualization tool, which had become indispensable for the many analysis of the contributions obtained by the use of the other described properties.

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Hoffmann, Salvaña Xavier Roderic. "Memory-induced complex contagion in spreading phenomena on networks". Doctoral thesis, Universitat de Barcelona, 2021. http://hdl.handle.net/10803/672674.

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Epidemic modeling has proven to be an essential framework for the study of contagion phenomena in biological, social, and technical systems. Albeit epidemic models have evolved into powerful predictive tools, most assume memoryless agents and independent transmission channels. Nevertheless, many real-life examples are manifestly time-sensitive and show strong correlations. Moreover, recent trends in agent-based modeling support a generalized shift from edge-based descriptions toward node-centric approaches. Here I develop an infection mechanism that is endowed with memory of past exposures and simultaneously incorporates the joint effect of multiple infectious sources. A notion of social reinforcement/inhibition arises organically, without being incorporated explicitly into the model. As a result, the concepts of non-Markovian dynamics and complex contagion become intrinsically coupled. I derive mean-field approximations for random degree-regular networks and perform extensive stochastic simulations for nonhomogeneous networks. The analysis of the SIS model reveals a sophisticated interplay between two memory modes, displayed by a collective memory loss and the dislocation of the critical point into two phase transitions. An intermediate region emerges where the system is either excitable or bistable, exhibiting fundamentally distinct behaviors compared to the customary healthy and endemic phases. Additionally, the transition to the endemic phase becomes hybrid, showing both continuous and discontinuous properties. These results provide renewed insights on the interaction between microscopic mechanisms and topological aspects of the underlying contact networks, and their joint effect on the properties of spreading processes. In particular, this type of modeling approach that combines memory effects and complex contagion could be suitable to describe ecological interactions between biological and social pathogens.
El modelatge epidèmic ha demostrat ser un marc essencial per a l’estudi dels fenòmens de contagi en sistemes biològics, socials i tècnics. Tot i que els models epidèmics han evolucionat cap a potents eines de predicció, la majoria assumeixen agents sense memòria i canals de transmissió independents. No obstant això, molts exemples de la vida real mostren fortes correlacions temporals i estructurals. A més, les tendències recents en la modelització basada en agents donen suport a un canvi generalitzat de les descripcions basades en els enllaços cap a enfocaments on els nodes són centrals. Aquí desenvolupo un mecanisme d’infecció dotat de memòria a exposicions passades i que simultàniament incorpora l’efecte conjunt de múltiples fonts infeccioses. Una noció de reforç/inhibició social sorgeix de manera orgànica, sense incorporar-se explícitament al model. Com a resultat, els conceptes de dinàmica no markoviana i contagi complex s’acoblen intrínsecament. Derivo aproximacions de camp mitjà per a xarxes aleatòries de grau fix i realitzo extenses simulacions estocàstiques per a xarxes no homogènies. L'anàlisi del model SIS revela una interacció sofisticada entre dos modes de memòria, que es manifesta mitjançant una pèrdua de memòria col·lectiva i la dislocació del punt crític en dues transicions de fase. Apareix una regió intermitja on el sistema és excitable o bistable, amb comportaments fonamentalment diferents en comparació amb les fases sanes i endèmiques habituals. A més, la transició a la fase endèmica esdevé híbrida, mostrant propietats contínues i també discontínues. Aquests resultats proporcionen una visió renovada sobre la interacció entre mecanismes microscòpics i aspectes topològics de les xarxes de contacte subjacents, i el seu efecte conjunt sobre les propietats dels processos de propagació. En particular, aquest tipus de modelització que combina efectes de memòria i contagi complex podria ser adequat per descriure interaccions ecològiques entre patògens biològics i socials.
El modelado epidémico ha demostrado ser un marco esencial para el estudio de los fenómenos de contagio en sistemas biológicos, sociales y técnicos. Aunque los modelos epidémicos han evolucionado hacia potentes herramientas de predicción, la mayoría asumen agentes sin memoria y canales de transmisión independientes. Sin embargo, muchos ejemplos de la vida real muestran fuertes correlaciones temporales y estructurales. Además, las tendencias recientes en la modelización basada en agentes apoyan un cambio generalizado de las descripciones basadas en los enlaces hacia enfoques donde los nodos son centrales.Aquí desarrollo un mecanismo de infección dotado de memoria a exposiciones pasadas y que simultáneamente incorpora el efecto conjunto de múltiples fuentes infecciosas. Una noción de refuerzo/inhibición social surge de manera orgánica, sin incorporarse explícitamente al modelo. Como resultado, los conceptos de dinámica no Markoviana y contagio complejo se acoplan intrínsecamente. Derivo aproximaciones de campo medio para redes aleatorias de grado fijo y realizo extensas simulaciones estocásticas para redes no homogéneas.El análisis del modelo SIS revela una interacción sofisticada entre dos modos de memoria, que se manifiesta mediante una pérdida de memoria colectiva y la dislocación del punto crítico en dos transiciones de fase. Aparece una región intermedia donde el sistema es excitable o bistable, con comportamientos fundamentalmente diferentes en comparación con las fases sanas y endémicas habituales. Además, la transición a la fase endémica se convierte en híbrida, mostrando propiedades continuas y también discontinuas.Estos resultados proporcionan una visión renovada sobre la interacción entre mecanismos microscópicos y aspectos topológicos de las redes de contacto subyacentes, y su efecto conjunto sobre las propiedades de los procesos de propagación. En particular, este tipo de modelización que combina efectos de memoria y contagio complejo podría ser adecuado para describir interacciones ecológicas entre patógenos biológicos y sociales.

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Kim, Hyoungshick. "Complex network analysis for secure and robust communications". Thesis, University of Cambridge, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.610134.

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Sagarra, Pascual Oleguer Josep. "Non-binary maximum entropy network ensembles and their application to the study of urban mobility". Doctoral thesis, Universitat de Barcelona, 2016. http://hdl.handle.net/10803/400560.

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Complex networks grow subject to structural constraints which affect their measurable properties. Assessing the effect that such constraints impose on their observables is thus a crucial aspect to be taken into account in their analysis, if one wants to quantify the effect a given topological property has on other observed network quantities observed in empirical datasets. Null models are needed for this end. A well understood analytical approach to face the generation and development of flexible models for binary networks is based on considering ensembles of networks obtained using an entropy maximization principle. In this work, we explore the generalization of maximum entropy ensembles to networks where multiple or non-dihcotomic connections among nodes are allowed. We develop a statistical mechanics framework where it is possible to get information about the most relevant observables given a large spectrum of linear and non-linear constraints including those depending both on the weight per link and their binary projection. We furthermore identify three different relevant cases that lead to distinctively different edge statistics, depending on the distinguishable nature of the events allocated to each link. For each case, we perform an extensive study considering microcanonical or hard constrained ensembles as well as grand canonical or soft constrained ones. We provide tools for the generation an analysis of network instances belonging to each model which are implemented and available in the form of open-source software packages, and we provide also analytical tools to obtain null model expectations to later compare to real data. Developing the theory developed, we apply the obtained insights to the analysis of urban mobility considering four large datasets of taxi displacements in the cities of New York, Singapore, San Francisco and Vienna. We show that, once they are appropriately transformed, mobility patterns are highly stable over long time scales and display common features across the studied datasets which are very conveniently represented using one of the cases earlier studied maximum entropy ensembles. We furthermore perform a critical review on existing mobility demand forecasting models and discuss their strengths and weaknesses when adapted to the urban environment, while showing how entropy maximizing models display the best descriptive power of the datasets using a number of network-based, information and matrix similarity metrics to assess the accuracy of the predicted vehicle flows. Based on our observations, we develop two practical applications based on our theoretical work. On the hand, we envisage a supersampling methodology to reliably extrapolate mobility records from a reduced sample which opens the possibility to scale up data from limited records when information on the full system is required. On the other hand, we adapt previous work on graph filtering to our proposed models that allows to extract random contributions from the observed empirical data. This allows to obtain simplified network backbones which contain the most relevant features of mobility datasets not explained by the considered constraints imposed in the maximum entropic models considered. Such a filter is useful for easing the analysis, computational handling and visualization of dense datasets, as well as assessing the degree of proximity between a model and empirical data using suitable hypothesis testing arguments.
Les xarxes complexes tenen una estructura complicada, on sovint es fa difícil establir les relacions de causalitat entre les seves propietats macroscòpiques (mesurables). Per tal de fer-ho es necessiten models nuls amb propietats flexibles que es puguin fixar. Per a xarxes amb connexions binàries (que tenen valor dicotòmic u o zero), s'han proposat col·lectivitats de xarxes que compleixen un principi de màxima entropia per a resoldre el problema de generació d'aquest tipus de models. En aquest treball explorem la seva generalització per a xarxes no-binàries, on les connexions entre elements estan graduades. Desenvolupem un tractament matemàtic que ens permet obtenir prediccions sobre els observables més rellevants d'una xarxa que tingui certes propietats prefixades, a triar en un rang ampli de funcions lineals i no-lineals pertanyent a col·lectivitats micro-canòniques (propietats fixades de manera estricta) i gran canòniques (propietats fixades sols en promig sobre la col·lectivitat). Detectem tres possibles varietats que duen a estadístiques d'ocupació d'enllaços diferents, depenent de la distingibilitat dels elements a partir del qual s'ha generat la xarxa. Per cada cas, desenvolupem eines per a la generació computacional i l'anàlisi de mostres de xarxes pertanyents a cada col·lectivitat. Tot seguit apliquem la teoria desenvolupada a l'anàlisi de mobilitat humana emprant sets de dades de desplaçaments de taxis a Nova York, Singapur, San Francisco i Viena. Mostrem l'estabilitat espaciotemporal de les dades estudiades i l'aparició de propietats comunes. Tot seguit realitzem un anàlisi crític de models de predicció de mobilitat existents i la seva possible adaptació als entorns urbans, mostrant com els models de màxima entropia tenen el major poder predictiu per descriure les dades. Finalment presentem dues aplicacions de la teoria desenvolupada que exploten les propietats comunes detectades a les dades estudiades. D'una banda, derivem un model que permet extrapolar dades de mobilitat sobre sets de dades reduïts. De l'altra, proposem un mètode de filtratge per extreure les contribucions de les dades reals dels trajectes esperats d'acord a qualssevol dels nostres models de màxima entropia. Aquest procediment permet obtenir versions simplificades de les xarxes originals que continguin les seves propietats més rellevants.

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Kasthurirathna, Dharshana Mahesh. "The influence of topology and information diffusion on networked game dynamics". Thesis, The University of Sydney, 2015. http://hdl.handle.net/2123/14570.

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This thesis studies the influence of topology and information diffusion on the strategic interactions of agents in a population. It shows that there exists a reciprocal relationship between the topology, information diffusion and the strategic interactions of a population of players. In order to evaluate the influence of topology and information flow on networked game dynamics, strategic games are simulated on populations of players where the players are distributed in a non-homogeneous spatial arrangement. The initial component of this research consists of a study of evolution of the coordination of strategic players, where the topology or the structure of the population is shown to be critical in defining the coordination among the players. Next, the effect of network topology on the evolutionary stability of strategies is studied in detail. Based on the results obtained, it is shown that network topology plays a key role in determining the evolutionary stability of a particular strategy in a population of players. Then, the effect of network topology on the optimum placement of strategies is studied. Using genetic optimisation, it is shown that the placement of strategies in a spatially distributed population of players is crucial in maximising the collective payoff of the population. Exploring further the effect of network topology and information diffusion on networked games, the non-optimal or bounded rationality of players is modelled using topological and directed information flow of the network. Based on the topologically distributed bounded rationality model, it is shown that the scale-free and small-world networks emerge in randomly connected populations of sub-optimal players. Thus, the topological and information theoretic interpretations of bounded rationality suggest the topology, information diffusion and the strategic interactions of socio-economical structures are cyclically interdependent.

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Savoy, Daniel Prata. "A dinâmica de opinião dos debates públicos em redes sociais complexas". Universidade de São Paulo, 2012. http://www.teses.usp.br/teses/disponiveis/100/100132/tde-04022013-114700/.

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Neste trabalho são estudados os efeitos, causados por variações na topologia de rede, no comportamento de quatro modelos de dinâmica de opinião: o Modelo Votante, o Modelo Confiança Limitada, o Modelo da Regra da Maioria e o Modelo CODA. Primeiramente estes modelos são utilizados em simulações que usam uma série de diversas redes sociais complexas, geradas para apresentar diferentes combinações de valores de certas propriedades chave, como aglomeração, conectividade, assortatividade e distâncias internas. Em seguida, são realizados experimentos que mostram como a topologia influencia os resultados na modelagem de cenários de debates públicos, onde duas opiniões rivais, A e B, disputam sob condições desiguais o consenso de uma população simulada.
This work studies the effects caused by variations in network topology in the behavior of four different models of opinion dynamics: the Voter Model, Bounded Confidence Model, the Majority Rule Model and the CODA Model. First, these models are used in simulations over a number of different complex social networks, generated to show sereval combinations of key properties such as clustering, connectivity, assortativity and path distances. Then, we perform experiments that show how the topology influences the results in modeling scenarios of public debates, where two rival opinions, A and B, compete under unequal conditions for the consensus of a simulated population.

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Bockholt, Mareike [Verfasser], i Katharina A. [Akademischer Betreuer] Zweig. "Analysis of network flows in complex networks / Mareike Bockholt ; Betreuer: Katharina A. Zweig". Kaiserslautern : Technische Universität Kaiserslautern, 2021. http://d-nb.info/1238074545/34.

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Vallès, Català Toni. "Network inference based on stochastic block models: model extensions, inference approaches and applications". Doctoral thesis, Universitat Rovira i Virgili, 2016. http://hdl.handle.net/10803/399539.

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L'estudi de xarxes ha contribuït a la comprensió de sistemes complexos en una àmplia gamma de camps com la biologia molecular i cel·lular, l'anatomia, la neurociència, l'ecologia, l'economia i la sociologia. No obstant, el coneixement disponible sobre molts sistemes reals encara és limitat, per aquesta raó el poder predictiu de la ciència en xarxes s'ha de millorar per disminuir la bretxa entre coneixement i informació. Per abordar aquest tema fem servir la família de 'Stochastic Block Models' (SBM), una família de models generatius que està guanyant gran interès recentment a causa de la seva adaptabilitat a qualsevol tipus de xarxa. L'objectiu d'aquesta tesi és el desenvolupament de noves metodologies d'inferència basades en SBM que perfeccionaran la nostra comprensió de les xarxes complexes. En primer lloc, investiguem en quina mesura fer un mostreg sobre models pot millorar significativament la capacitat de predicció que considerar un únic conjunt òptim de paràmetres. Un cop sabem quin model és capaç de descriure millor una xarxa determinada, apliquem aquest mètode en un cas particular d'una xarxa real: una xarxa basada en les interaccions/sutures entre els ossos del crani en nounats. Concretament, descobrim que les sutures tancades a causa d'una malaltia patològica en el nounat humà son menys probables, des d'un punt de vista morfològic, que les sutures tancades sota un desenvolupament normal. Recents investigacions en xarxes multicapa conclou que el comportament de les xarxes d'una sola capa són diferents de les de múltiples capes; d'altra banda, les xarxes del món real se'ns presenten com xarxes d'una sola capa.
El estudio de las redes del mundo real han empujado hacia la comprensión de sistemas complejos en una amplia gama de campos como la biología molecular y celular, la anatomía, la neurociencia, la ecología, la economía y la sociología . Sin embargo, el conocimiento disponible de muchos sistemas reales aún es limitado, por esta razón el poder predictivo de la ciencia en redes se debe mejorar para disminuir la brecha entre conocimiento y información. Para abordar este tema usamos la familia de 'Stochastic Block Modelos' (SBM), una familia de modelos generativos que está ganando gran interés recientemente debido a su adaptabilidad a cualquier tipo de red. El objetivo de esta tesis es el desarrollo de nuevas metodologías de inferencia basadas en SBM que perfeccionarán nuestra comprensión de las redes complejas. En primer lugar, investigamos en qué medida hacer un muestreo sobre modelos puede mejorar significativamente la capacidad de predicción a considerar un único conjunto óptimo de parámetros. Seguidamente, aplicamos el método mas predictivo en una red real particular: una red basada en las interacciones/suturas entre los huesos del cráneo humano en recién nacidos. Concretamente, descubrimos que las suturas cerradas a causa de una enfermedad patológica en recién nacidos son menos probables, desde un punto de vista morfológico, que las suturas cerradas bajo un desarrollo normal. Concretamente, descubrimos que las suturas cerradas a causa de una enfermedad patológica en recién nacidos son menos probables, desde un punto de vista morfológico, que las suturas cerradas bajo un desarrollo normal. Recientes investigaciones en las redes multicapa concluye que el comportamiento de las redes en una sola capa son diferentes a las de múltiples capas; por otra parte, las redes del mundo real se nos presentan como redes con una sola capa. La parte final de la tesis está dedicada a diseñar un nuevo enfoque en el que dos SBM separados describen simultáneamente una red dada que consta de una sola capa, observamos que esta metodología predice mejor que la metodología de un SBM solo.
The study of real-world networks have pushed towards to the understanding of complex systems in a wide range of fields as molecular and cell biology, anatomy, neuroscience, ecology, economics and sociology. However, the available knowledge from most systems is still limited, hence network science predictive power should be enhanced to diminish the gap between knowledge and information. To address this topic we handle with the family of Stochastic Block Models (SBMs), a family of generative models that are gaining high interest recently due to its adaptability to any kind of network structure. The goal of this thesis is to develop novel SBM based inference approaches that will improve our understanding of complex networks. First, we investigate to what extent sampling over models significatively improves the predictive power than considering an optimal set of parameters alone. Once we know which model is capable to describe better a given network, we apply such method in a particular real world network case: a network based on the interactions/sutures between bones in newborn skulls. Notably, we discovered that sutures fused due to a pathological disease in human newborn were less likely, from a morphological point of view, that those sutures that fused under a normal development. Recent research on multilayer networks has concluded that the behavior of single-layered networks are different from those of multilayer ones; notwhithstanding, real world networks are presented to us as single-layered networks. The last part of the thesis is devoted to design a novel approach where two separate SBMs simultaneously describe a given single-layered network. We importantly find that it predicts better missing/spurious links that the single SBM approach.

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Garuccio, Elena. "Reconstruction, modelling and analysis of economic networks". Doctoral thesis, Università di Siena, 2018. http://hdl.handle.net/11365/1059854.

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In Chapter 1 We present the mathematical and theoretical framework to define a universally renormalizable model of complex network, which we prove to be consistent with the fitness model. We also show how the model leads to Lévy-stable fitness distributions and random scale-free networks if the hidden variables are resampled at each renormalization. By contrast, we show how the model, with fixed fitness parameters, naturally describes real-world networks. Beside the theoretical framework for the network topology, we also provide a model for the reconstruction of links weight based on a modified version of the gravity model. In Chapter 2 We apply our universally rescaling model for complex networks to two main economic networks. Firstly we analyze both the binary undirected and weighted directed World Trade Network. Secondly, we study the the elec- tronic Market for Internet Deposit for the Italian bank. The former describes trade between countries and the latter reports financial transactions between Italian banks for the period of one year. In this chapter we show how our model performs in reconstructing both topological and weighted properties of these networks and of their coarse grained representation. In Chapter 3 we apply a community detection algorithm for correlation matrices, based on Random Matrix Theory, to study community structures in the United Nations Sustainable Development Goals (UN-SDG) indicators. We discuss the issue of competing indicators which seems to be confirmed by thefounding of communities that are highly correlated internally and poorly corre- lated with the members of the external groups.

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Ciotti, Valerio. "Positive and negative connections and homophily in complex networks". Thesis, Queen Mary, University of London, 2018. http://qmro.qmul.ac.uk/xmlui/handle/123456789/31787.

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In this thesis I investigate the effects of positive and negative connections on social and organization networks, and the presence and role of homophily in networks of scientific collaborations and citations through the combination of methodologies borrowed from complexity science, statistics, and organizational sciences. In the first part of the thesis, I study the differences between patterns of positive and negative connections among individuals in two online signed social networks. Findings suggest that the sign of links in a social network shapes differently the network's topology: there is a positive correlation between the degrees of two nodes, when they share a positive connection, and a negative correlation when they share a negative connection. I then move my focus to the study of a dataset on start-ups from which I construct and analyse the competition and mobility networks among companies. Results show that the presence of competition has negative effects on the mobility of people among companies and on the success of the start-up ecosystem of a nation. Competitive behaviours may also emerge in science. Therefore, in the second part of this thesis, I focus on a database of all papers and authors who have published in the American Physical Society (APS) journals. Through the analysis of the citation network of the APS, I propose a method that aims to statistically validate the presence (or absence) of a citation between any two articles. Results show that homophily is an important mechanism behind the citation between articles: the more two articles share similar bibliographies, i.e., deal with similar arguments, the more likely there is a citation between them. In the last chapter, I investigate the presence of homophily in the APS data set, this time at the level of the collaboration network among sci- entists. Results show that homophily can be responsible in fostering collaboration, but above a given point the effect of similarity decreases the probability of a collaboration. Additionally, I propose a model that successfully reproduces the empirical findings.

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Pimenta, Mayra Mercedes Zegarra. "Self-organization map in complex network". Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-30102018-111955/.

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The Self-Organization Map (SOM) is an artificial neural network that was proposed as a tool for exploratory analysis in large dimensionality data sets, being used efficiently for data mining. One of the main topics of research in this area is related to data clustering applications. Several algorithms have been developed to perform clustering in data sets. However, the accuracy of these algorithms is data depending. This thesis is mainly dedicated to the investigation of the SOM from two different approaches: (i) data mining and (ii) complex networks. From the data mining point of view, we analyzed how the performance of the algorithm is related to the distribution of data properties. It was verified the accuracy of the algorithm based on the configuration of the parameters. Likewise, this thesis shows a comparative analysis between the SOM network and other clustering methods. The results revealed that in random configuration of parameters the SOM algorithm tends to improve its acuracy when the number of classes is small. It was also observed that when considering the default configurations of the adopted methods, the spectral approach usually outperformed the other clustering algorithms. Regarding the complex networks approach, we observed that the network structure has a fundamental influence of the algorithm accuracy. We evaluated the cases at short and middle learning time scales and three different datasets. Furthermore, we show how different topologies also affect the self-organization of the topographic map of SOM network. The self-organization of the network was studied through the partitioning of the map in groups or communities. It was used four topological measures to quantify the structure of the groups such as: modularity, number of elements per group, number of groups per map, size of the largest group in three network models. In small-world (SW) networks, the groups become denser as time increases. An opposite behavior is found in the assortative networks. Finally, we verified that if some perturbation is included in the system, like a rewiring in a SW network and the deactivation model, the system cannot be organized again. Our results enable a better understanding of SOM in terms of parameters and network structure.
Um Mapa Auto-organizativo (da sigla SOM, Self-organized map, em inglês) é uma rede neural artificial que foi proposta como uma ferramenta para análise exploratória em conjuntos de dados de grande dimensionalidade, sendo utilizada de forma eficiente na mineração de dados. Um dos principais tópicos de pesquisa nesta área está relacionado com as aplicações de agrupamento de dados. Vários algoritmos foram desenvolvidos para realizar agrupamento de dados, tendo cada um destes algoritmos uma acurácia específica para determinados tipos de dados. Esta tese tem por objetivo principal analisar a rede SOM a partir de duas abordagens diferentes: mineração de dados e redes complexas. Pela abordagem de mineração de dados, analisou-se como o desempenho do algoritmo está relacionado à distribuição ou características dos dados. Verificou-se a acurácia do algoritmo com base na configuração dos parâmetros. Da mesma forma, esta tese mostra uma análise comparativa entre a rede SOM e outros métodos de agrupamento. Os resultados revelaram que o uso de valores aleatórios nos parâmetros de configuração do algoritmo SOM tende a melhorar sua acurácia quando o número de classes é baixo. Observou-se também que, ao considerar as configurações padrão dos métodos adotados, a abordagem espectral usualmente superou os demais algoritmos de agrupamento. Pela abordagem de redes complexas, esta tese mostra que, se considerarmos outro tipo de topologia de rede, além do modelo regular geralmente utilizado, haverá um impacto na acurácia da rede. Esta tese mostra que o impacto na acurácia é geralmente observado em escalas de tempo de aprendizado curto e médio. Esse comportamento foi observado usando três conjuntos de dados diferentes. Além disso, esta tese mostra como diferentes topologias também afetam a auto-organização do mapa topográfico da rede SOM. A auto-organização da rede foi estudada por meio do particionamento do mapa em grupos ou comunidades. Foram utilizadas quatro medidas topológicas para quantificar a estrutura dos grupos em três modelos distintos de rede: modularidade, número de elementos por grupo, número de grupos por mapa, tamanho do maior grupo. Em redes de pequeno mundo, os grupos se tornam mais densos à medida que o tempo aumenta. Um comportamento oposto a isso é encontrado nas redes assortativas. Apesar da modularidade, tem um alto valor em ambos os casos.

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Dias, Eduardo de Souza. "Estudo do risco sistêmico em redes interbancárias pela abordagem de sistemas complexos". Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/100/100132/tde-05012016-225818/.

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O estudo econômico e financeiro vem se modificando e buscando novas metodologias. Desde a crise que se iniciou com os \"subprimes\" nos Estados Unidos em 2008 e se espalhou para as economias de todo o mundo, novas discussões de como ela poderia ter sido evitada e qual caminho deveriam os países seguir para sair da estagnação já surgem no mundo acadêmico em direção ao estudo da complexidade. Em termos econômicos, algumas críticas feitas ao estudo da economia tradicional, principalmente atribuídas ao excesso de restrições utilizados nos modelos, podem ser agora afrouxadas, uma a uma, através de modelagens baseadas em agentes. Já no entendimento e controle do risco financeiro, redes complexas prestam fundamental distinção. Os modelos até então utilizados para controle de riscos no mercado financeiro não levam em consideração o risco global, porém apenas o risco local. Muitas teorias sobre a diminuição do risco através da diversificação são aceitas e realmente produzem sistemas mais estáveis, porém com pouca resiliência, ou seja, o número de crises diminui, porém as que ocorrem são muito mais graves. Este trabalho sugeriu um modelo baseado em agentes, onde um sistema econômico simples foi construído, para ser capaz de gerar crises. Este modelo formado por firmas e demanda estocástica, utiliza bancos para simular o mercado financeiro. Tais bancos estão conectados entre si através de uma rede interbancária. Para testar os efeitos de risco sistêmico, foram realizados três testes. No primeiro aumentou-se a alavancagem máxima permitida e os bancos conseguiram obter mais lucro e maior crescimento, porém a partir de certo patamar o sistema entrou em colapso, com frequente crises. No segundo aumentou-se a conectividade média e os bancos também obtiveram maior lucro, porém com crises muito mais profundas. No aumento do índice de cluster da rede interbancária, assim como nos dois primeiros testes os bancos conseguiram maior crescimento, porém agora sem os mesmos efeitos indesejáveis causados pelo aumento do risco.
Economic and financial studies have been changing and searching new methodologies. Since the 2008 subprime crisis, which spread into economies around the globe, new discussions on how it could have been prevented, and paths which countries should follow to emerge from stagflation have been discussed by the academic world towards the complexity subject. In economic terms, some of the criticism of neoclassic economics, mainly due to excessive constrains used by its models, can now be eased, one by one, through agent based modeling. Regarding financial risk understanding and control, complex networks assume fundamental distinction. Models applied so far in financial market risk control dont consider global risk, but only the local one. Many theories on risk diversification are accepted and indeed produce more stable systems, although with little resilience, which means smaller number of crisis, but when it does occur, are more serious ones. This paper suggested an agent based model, using a simple economic system capable of generating crisis. This model was constituted by firms and stochastic demand, using banks to simulate the financial market. These banks were connected though a banking network. In order to test systemic risk, the model performed three tests. First, the maximum leverage allowed was increased and banks were able to achieve higher profits and growth, but from a certain level, the system collapsed with frequent crisis. Second, the average connectivity was increased and banks obtained higher profits, however with more severe crisis. Finally, increasing banking network cluster index, similarly to the first two tests, banks achieved higher growth, but without the undesirable effects caused by risk increase.

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Holovatch, T. "Complex transportation networks : resilience, modelling and optimisation". Thesis, Coventry University, 2011. http://curve.coventry.ac.uk/open/items/eafefd84-ff08-43cf-a544-597ee5e63237/1.

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Hill, Robert M. Martin Barbara N. "Leadership capacity in a complex connected age". Diss., Columbia, Mo. : University of Missouri--Columbia, 2009. http://hdl.handle.net/10355/7033.

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Title from PDF of title page (University of Missouri--Columbia, viewed on Feb 26, 2010). The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file. Dissertation advisor: Dr. Barbara N. Martin. Vita. Includes bibliographical references.

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Oh, Se-Wook. "Complex contagions with lazy adoption". Thesis, University of Oxford, 2017. https://ora.ox.ac.uk/objects/uuid:207c7ce3-d4fb-4657-8386-4e5174a8b7dc.

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Weighill, Deborah A. "Exploring the topology of complex phylogenomic and transcriptomic networks". Thesis, Stellenbosch : Stellenbosch University, 2014. http://hdl.handle.net/10019.1/95800.

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Thesis (MSc)--Stellenbosch University, 2014.
ENGLISH ABSTRACT: This thesis involved the development and application of network approaches for the construction, analysis and visualization of phylogenomic and transcriptomic networks. A co-evolutionary network model of grapevine genes was constructed based on three mechanisms of evolution. The investigation of local neighbourhoods of this network revealed groups of functionally related genes, illustrating that the multi-mechanism evolutionary model was identifying groups of potentially co-evolving genes. An extended network definition, namely 3-way networks, was investigated, in which edges model relationships between triplets of objects. Strategies for weighting and pruning these 3-way networks were developed and applied to a phylogenomic dataset of 211 bacterial genomes. These 3-way bacterial networks were compared to standard 2-way network models constructed from the same dataset. The 3-way networks modelled more complex relationships and revealed relationships which were missed by the two-way network models. Network meta-modelling was explored in which global network and node-bynode network comparison techniques were applied in order to investigate the effect of the similarity metric chosen on the topology of multiple types of networks, including transcriptomic and phylogenomic networks. Two new network comparison techniques were developed, namely PCA of Topology Profiles and Cross-Network Topological Overlap. PCA of Topology Profiles compares networks based on a selection of network topology indices, whereas Cross- Network Topological Overlap compares two networks on a node-by-node level, identifying nodes in two networks with similar neighbourhood topology and thus highlighting areas of the networks with conflicting topologies. These network comparison methods clearly indicated how the similarity metric chosen to weight the edges of the network influences the resulting network topology, consequently influencing the biological interpretation of the networks.
AFRIKAANSE OPSOMMING: Hierdie tesis hou verband met die ontwikkeling en toepassing van netwerk benaderings vir die konstruksie, analise en visualisering van filogenomiese en transkriptomiese netwerke. 'n Mede-evolusionêre netwerk model van wingerdstok gene is gebou, gebaseerd op drie meganismes van evolusie. Die ondersoek van plaaslike omgewings van die netwerk het groepe funksioneel verwante gene aan die lig gebring, wat daarop dui dat die multi-meganisme evolusionêre model groepe van potensieele mede-evolusieerende gene identifiseer. 'n Uitgebreide netwerk definisie, naamliks 3-gang netwerke, is ondersoek, waarin lyne die verhoudings tussen drieling voorwerpe voorstel. Strategieë vir weeg en snoei van hierdie 3-gang netwerke was ontwikkel en op 'n filogenomiese datastel van 211 bakteriële genome toegepas. Hierdie 3-gang bakteriële netwerke is met die standaard 2-gang netwerk modelle wat saamgestel is uit dieselfde datastel vergelyk. Die 3-gang netwerke het meer komplekse verhoudings gemodelleer en het verhoudings openbaar wat deur die tweerigting-netwerk modelle gemis is. Verder is netwerk meta-modellering ondersoek waarby globalle netwerk en punt-vir-punt netwerk vergelykings tegnieke toegepas is, met die doel om die effek van die ooreenkoms-maatstaf wat gekies is op die topologie van verskeie tipes netwerke, insluitend transcriptomic en filogenomiese netwerke, te bepaal. Twee nuwe netwerk-vergelyking tegnieke is ontwikkel, naamlik "PCA of Topology Profiles" en"Cross-Network Topological Overlap". PCA van Topologie Profiele vergelyk netwerke gebaseer op 'n seleksie van netwerk topologie indekse, terwyl Cross-netwerk Topologiese Oorvleuel vergelyk twee netwerke op 'n punt-vir-punt vlak, en identifiseer punte in twee netwerke met soortgelyke lokale topologie en dus lê klem op gebiede van die netwerke met botsende topologieë. Hierdie netwerk-vergelyking metodes dui duidelik aan hoe die ooreenkoms maatstaf wat gekies is om die lyne van die netwerk gewig te gee, die gevolglike netwerk topologie beïnvloed, wat weer die biologiese interpretasie van die netwerke kan beïnvloed.

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Gattaz, Cristiane Chaves. "Um modelo de referência de formação e gestão de redes organizacionais: o caso do sistema de C,T&I do setor aeroespacial brasileiro". Universidade de São Paulo, 2010. http://www.teses.usp.br/teses/disponiveis/3/3136/tde-10012011-095422/.

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A presente pesquisa tem como objetivo construir uma abordagem conceitual de formação e gestão de uma dada rede de cooperação organizacional (RCO). O referencial teórico que sustenta esta pesquisa fundamenta-se nas mais recentes contribuições conceituais embasadas nos paradigmas de Redes Complexas, Redes Sócio-econômicas, Redes Organizacionais e Gestão Organizacional. Considerado como uma das ações de gestão prioritárias do governo brasileiro, utiliza-se como estudo de caso único para essa pesquisa a formação de uma rede de cooperação inter-organizacional que possa promover a inserção do Brasil na inovação em nanotecnologia aplicada em cargas úteis e satélites. Os agentes que representam significativamente o Sistema de Ciência, Tecnologia e Inovação (C,T&I) do setor aeroespacial no Brasil a AEB, CGEE, INPE, UnB, MCT, IPEA, MPOG, CTA (atual DCTA) são entrevistados de forma semi-estruturada e os relatórios técnicos mais relevantes ao caso são analisados. Em função da maior capacidade de consolidação dos dados obtidos respeitando os constructos propostos neste trabalho, os dados são apresentados utilizando-se de uma tecnologia de software de modelagem organizacional, PArchitect, em parceria com a Society of Design and Process Science. Em síntese, as evidências confirmam os pressupostos desta pesquisa caracterizando a RCO como sendo um sistema sócio-econômico, complexo e dinâmico, considerando as modalidades de cooperação técnico-produtiva, interorganizacional e tecnológica, movidas por cultura e competitividade. Além disso, o próprio processo de formação de uma dada RCO contribui para a dinâmica de sua gestão. Como resultado, essa pesquisa apresenta um modelo de referência de formação e gestão de redes de cooperação organizacional, com destaque ao caso, contribuindo com a compreensão da sua dinâmica, sob enfoque sistêmico; a localização e interpretação de mudanças e impactos na rede em um determinado contexto; e a correção em tempo hábil da direção da rede em torno do seu objetivo comum em um determinado contexto.
This study aims to build a conceptual approach for the design and management of a given business cooperation network (BCN). The theoretical framework that supports this research is organized upon the most recent conceptual contributions based on the paradigms of Complex Networks, Socio-economic Networks, Organizational Networks and Organizational Management. Regarded as one of the priority actions for management of the Brazilian government, the design of an inter-organizational cooperation network that could promote the insertion of Brazil in innovation in Nanotechnology applied to Payloads and Satellites is used as a unique case study for this research. The agents who represent significantly the Science, Technology & Innovation (S,T&I) System of the Brazilian Aerospace Sector - the AEB, CGEE, INPE, UnB, MCT, IPEA, MPOG, CTA (current DCTA) are interviewed in a semistructured technique and the most relevant technical reports of the case are analyzed. Due to the greater ability to consolidate the data obtained in compliance with the constructs proposed in this work, the data are presented using a software technology for organizational modeling named PArchitect in partnership with the Society of Design and Process Science. In summary, the evidence confirms the assumptions of this research characterizing the BCN as a complex and dynamic socio-economic system, considering the modalities of technical-productive, interorganizational and technological cooperation, driven by culture and competitiveness. Moreover, the process of designing a given BCN contributes to the dynamics of management. As a result, this study provides a reference model for the design and network management for organizational cooperation, highlighting the case. It contributes to the understanding of the BCN dynamics under a systemic approach; the location and interpretation of changes and impacts on the network at a given context; and the correction in a timely manner of the direction of the network around their common goal in a given context.

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Jorge, Fabricio Gava de Almeida. "Redes Empresariais e Sustentabilidade: modelos baseados em agentes para análise da difusão de estratégias no ambiente competitivo". Universidade de São Paulo, 2014. http://www.teses.usp.br/teses/disponiveis/100/100132/tde-15122014-120056/.

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Com a divulgação dos efeitos antrópicos sobre o clima nos últimos anos, nota-se um adensamento no debate acerca da incorporação da temática socioambiental na agenda corporativa. Embora as externalidades ambientais da atividade produtiva sejam conhecidas desde o início da Revolução Industrial, os processos de elaboração e implementação de estratégias empresariais de sustentabilidade ainda é algo em desenvolvimento. O presente trabalho visa analisar a dinâmica da difusão de estratégias de sustentabilidade em redes empresariais através de modelos de sistemas sociais complexos. Para tanto, são analisados alguns modelos consolidados na literatura, como o modelo de Ising (1925), Barabási-Albert (1999) e Ito e Kaneko (2002). Tal análise subsidia a criação de um modelo específico, cujos resultados de sua simulação são utilizados para gerar hipóteses que alicerceiam a elaboração de cenários prospectivos, pautando-se no referencial de Berger (1959) e Godet (2008). Por fim, tais cenários apresentam possíveis realidades futuras quanto à emergência de um setor produtivo mais sustentável, auxiliando no planejamento de empresas e governos.
With the disclosure of anthropogenic impacts on climate in recent years, there has been a growing debate about the incorporation of environmental issues on the corporate agenda. Although the environmental externalities of productive activity are known since the beginning of the Industrial Revolution, the processes of development and implementation of corporate sustainability strategies are still under development. The present work analyzes the dynamics of the diffusion of sustainability strategies on enterprise networks through models of complex social systems. Hence, we analyze three well known models: Ising (1925), Barabási-Albert (1999) and Ito and Kaneko (2002). This analysis underpins the creation of a specific model, which results are used to generate hypotheses that support the development of prospective scenarios, based on Berger (1959) and Godet (2008). Finally, these scenarios present possible future realities for the emergence of a sustainable productive sector, assisting in the planning of businesses and governments.

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Al-Musawi, Ahmad Jr. "COMPLEX NETWORK GROWING MODEL USING DOWNLINK MOTIFS". VCU Scholars Compass, 2013. http://scholarscompass.vcu.edu/etd/3088.

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Understanding the underlying architecture of gene regulatory networks (GRNs) has been one of the major goals in systems biology and bioinformatics as it can provide insights in disease dynamics and drug development. Such GRNs are characterized by their scale-free degree distributions and existence of network motifs, which are small subgraphs of specific types and appear more abundantly in GRNs than in other randomized networks. In fact, such motifs are considered to be the building blocks of GRNs (and other complex networks) and they help achieve the underlying robustness demonstrated by most biological networks. The goal of this thesis is to design biological network (specifically, GRN) growing models. As the motif distribution in networks grown using preferential attachment based algorithms do not match that of the GRNs seen in model organisms like E. coli and yeast, we hypothesize that such models at a single node level may not properly reproduce the observed degree and motif distributions of biological networks. Hence, we propose a new network growing algorithm wherein the central idea is to grow the network one motif (specifically, we consider one downlink motif) at a time. The accuracy of our proposed algorithm was evaluated extensively and show much better performance than existing network growing models both in terms of degree and motif distributions. We also propose a complex network growing game that can identify important strategies behind motif interactions by exploiting human (i.e., gamer) intelligence. Our proposed gaming software can also help in educational purposes specifically designed for complex network studies.

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Matamalas, Llodrà Joan Tomàs. "Higher-order dynamics on complex networks". Doctoral thesis, Universitat Rovira i Virgili, 2019. http://hdl.handle.net/10803/666484.

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L’estudi de les xarxes complexes ha esdevingut un nou paradigma a l’hora d’entendre i modelar sistemes físics. Uns dels principals punts d’interès són les dinàmiques que hi podem modelar. Però com en tot model, la quantitat de informació que podem representar-hi està limitada per la seva complexitat. La motivació principal d’aquesta tesi és l’estudi de l’efecte que un increment de la complexitat estructural, relacional i temporal té sobre tres importants àrees d’estudi: l’evolució de la cooperació, la propagació de malalties, i l’estudi de la mobilitat humana. En aquest treball hem utilitzat dilemes socials per estudiar com evoluciona la cooperació dins d’una població. Incrementant l’ordre de complexitat estructural de les xarxes, permetent que els individus és puguin relacionar en diferents contextos socials, s’ha mostrat cabdal a l’hora d’explicar algunes característiques sobre l’aparició de comportaments altruistes. Utilitzant aquestes noves estructures, les xarxes multicapa, permetem als membres de la població cooperar en determinat contextos i de no fer-ho en d’altres i això, com analíticament demostrem, augmenta l’espectre d’escenaris allà on cooperació i defecció poden sobreviure. Seguidament, estudiem els models de propagació de malalties des de el punt de vista dels enllaços entre individus. Amb aquest augment de la complexitat relacional dels models epidèmics, aconseguim extreure informació que ens permet, entre altres coses, definir una mesura d’influència d’un enllaç a la propagació de l’epidèmia. Utilitzem aquest fet per a proposar una nova mesura de contenció, basada en l’eliminació dels enllaços més influents, que es mostra més eficient que altres mètodes previs. Finalment, proposem un mètode per a descriure la mobilitat que permet capturar patrons recurrents i heterogeneïtats en els temps que els individus estan en un lloc abans de desplaçar-se a un altre. Aquestes propietats són intrínseques a la mobilitat humana i el fet de poder-les capturar, tot i el cost d’augmentar l’ordre temporal, és crític, com demostrem, a l’hora de modelar com les epidèmies és difonen per mitja del moviment de les persones.
El estudio de redes complejas se ha convertido en un nuevo paradigma para comprender y modelar sistemas físicos. Uno de los principales puntos de interés son las dinámicas que podemos modelar. Pero como en todo modelo, la cantidad de información que podemos representar está limitada por su complejidad. La motivación principal de esta tesis es estudiar el efecto que un incremento de la complejidad estructural, relacional y temporal tiene sobre tres importantes áreas de estudio: la evolución de la cooperación, la propagación de enfermedades, y el estudio de la movilidad humana. En este trabajo hemos utilizado dilemas sociales para estudiar cómo evoluciona la cooperación dentro de una población. Incrementando el orden de complejidad estructural de las redes, permitiendo que los individuos se puedan relacionar en diferentes contextos sociales, se ha demostrado capital para explicar algunas de las características sobre la aparición de comportamientos altruistas. Utilizando estas nuevas estructuras, las redes multicapa, permitimos a los miembros de la población cooperar en determinados contextos y no hacerlo en otros, con lo que, como demostramos analíticamente, aumenta el espectro de escenarios en los que la cooperación y la defección pueden sobrevivir. A continuación, estudiamos modelos de propagación de enfermedades desde el punto de vista de los enlaces entre individuos. Con este aumento de complejidad relacional de los modelos epidémicos, conseguimos extraer información que nos permite, entre otras cosas, definir una medida de contención, basada en la eliminación de los enlaces más influyentes, que se muestra más eficaz que otros métodos previos. Finalmente, proponemos un método para describir la movilidad que permite capturar patrones recurrentes y heterogeneidades en los tiempos que los individuos están en un lugar antes de desplazarse a otro. Estas propiedades son intrínsecas a la movilidad humana y el hecho de poder capturarlas, a pesar de incrementar el orden temporal, es crítico, como demostramos, para modelar cómo las epidemias se difunden por medio del movimiento de las personas.
The study of complex networks has become a new paradigm to understand and model physical systems. One of the points of interest is the dynamics that we can model. However, as with any model, the amount of information that we can represent is limited by its complexity. The primary motivation of this thesis is the study of the effect that an increase in structural, relational and temporal complexity has on three critical areas of study: the evolution of cooperation, epidemic spreading and human mobility. In this work, we have used social dilemmas to study how cooperation within a population evolves. Increasing the order of structural complexity of the networks, allowing individuals to interact in different social contexts, has shown to be crucial to explain some features about the emergence of altruistic behaviors. Using these new structures, multilayer networks, we allow members of the population to cooperate in specific contexts and defect in others, and this, as we analytically demonstrate, increases the spectrum of scenarios where both strategies can survive. Next, we study the models of epidemic spreading from the point of view of the links between individuals. With this increase in the relational complexity of the epidemic models, we can extract information that allows us, among other things, to define a measure of the contribution of a link to the spreading. We use this metric to propose a new containment measure, based on the elimination of the most influential links, which is more effective than other previous methods. Finally, we propose a method to describe mobility that allows capturing recurrent and heterogeneous patterns in the times that individuals stay in a place before moving to another. These properties are intrinsic to human mobility, and the fact of being able to capture them, despite the cost of increasing the temporal order is critical, as we demonstrate, when it comes to modeling how epidemics spread through the movement of the people.

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Araújo, Eduardo Barbosa. "Scientific Collaboration Networks from Lattes Database: Topology, Dynamics and Gender Statistics". reponame:Repositório Institucional da UFC, 2016. http://www.repositorio.ufc.br/handle/riufc/18489.

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ARAÚJO, Eduardo Barbosa. Scientific Collaboration Networks from Lattes Database: Topology, Dynamics and Gender Statistics. 2016. 88 f. Tese (Doutorado em Física) - Programa de Pós-Graduação em Física, Departamento de Física, Centro de Ciências, Universidade Federal do Ceará, Fortaleza, 2016.
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Understanding the dynamics of research production and collaboration may reveal better strategies for scientific careers, academic institutions and funding agencies. Here we propose the use of a large and multidisciplinary database of scientific curricula in Brazil, namely, the Lattes Platform, to study patterns of scientific production and collaboration. Detailed information about publications and researchers is available in this database. Individual curricula are submitted by the researchers themselves so that co-authorship is unambiguous. Researchers can be evaluated by scientific productivity, geographical location and field of expertise. Our results show that the collaboration network is growing exponentially for the last three decades, with a distribution of number of collaborators per researcher that approaches a power-law as the network gets older. Moreover, both the distributions of number of collaborators and production per researcher obey power-law behaviors, regardless of the geographical location or field, suggesting that the same universal mechanism might be responsible for network growth and productivity. We also show that the collaboration network under investigation displays a typical assortative mixing behavior, where teeming researchers (i.e., with high degree) tend to collaborate with others alike. Moreover, we discover that on average men prefer collaborating with other men than with women, while women are more egalitarian. This is consistently observed over all fields and essentially independent on the number of collaborators of the researcher. The solely exception is for engineering, where clearly this gender bias is less pronounced, when the number of collaborators increases. We also find that the distribution of number of collaborators follows a power-law, with a cut-off that is gender dependent. This reflects the fact that on average men produce more papers andhave more collaborators than women. We also find that both genders display the same tendency towards interdisciplinary collaborations, except for Exact and Earth Sciences, where women having many collaborators are more open to interdisciplinary research.
Compreender a dinâmica de produção e colaboração em pesquisa pode revelar melhores estratégias para carreiras científicas, instituições acadêmicas e agências de fomento. Neste trabalho nós propomos o uso de uma grande e multidisciplinar base de currículos científicos brasileira, a Plataforma Lattes, para o estudo de padrões em pesquisa científica e colaborações. Esta base de dados inclui informações detalhadas acerca de publicações e pesquisadores. Currículos individuais são enviados pelos próprios pesquisadores de forma que a identificação de coautoria não é ambígua. Pesquisadores podem ser classificados por produção científica, localização geográfica e áreas de pesquisa. Nossos resultados mostram que a rede de colaborações científicas tem crescido exponencialmente nas últimas três décadas, com a distribuição do número de colaboradores por pesquisador se aproximando de uma lei de potência à medida que a rede evolui. Além disso, ambas a distribuição do número de colaboradores e a produção por pesquisador seguem o comportamento de leis de potência, independentemente da região ou áreas, sugerindo que um mesmo mecanismo universal pode ser responsável pelo crescimento da rede e pela produtividade dos pesquisadores. Também mostramos que as redes de colaboração investigadas apresentam um típico comportamento assortativo, no qual pesquisadores de alto nível (com muitos colaboradores) tendem a colaborador com outros semelhantes. Em seguida, mostramos que homens preferem colaborar com outros homens enquanto mulheres são mais igualitárias ao estabelecer suas colaborações. Isso é consistentemente observado em todas as áreas e é essencialmente independente do número de colaborações do pesquisador. A única exceção sendo a área de Engenharia, na qual este viés é claramente menos pronunciado para pesquisadores com muitas colaborações. Também mostramos que o número de colaborações segue o comportamento de leis de potência, com um cutoff dependente do gênero. Isso se reflete no fato de que em média mulheres produzem menos artigos e têm menos colaborações que homens. Também mostramos que ambos os gêneros exibem a mesma tendência quanto a colaborações interdisciplinares, exceto em Ciências Exatas e da Terra, nas quais mulheres tendo mais colaboradores são mais propensas a pesquisas interdisciplinares.

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Abreu, Luís Fernando Dorelli de. "Estrutura e dinâmica de redes de informação". Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-08112016-091004/.

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O aumento na disponibilidade de dados referentes a interação entre pessoas online tornou possível o estudo o processo de propagação de informações em redes sociais com volumes de dado antes jamais pensados. Neste trabalho, utilizamos dados do site de micro-blogging Twitter juntamente com conceitos de redes complexas para entender, caracterizar e classificar processos de difusão de informação observados nessa plataforma e em redes sociais em geral. Apresentamos importantes medidas para caracterização de cascatas de informação, bem como algoritmos eficientes para o seu cálculo. Com o auxilio dessas, mostramos que é possível quantificar a influência da rede social no processo de propagação de informação. Em seguida, constatamos que a informação tende a propagar por caminhos mínimos nessa rede. Por fim, mostramos que é possível utilizar apenas a topologia da rede social, sem nenhuma informação semântica, para agrupar tópicos, e que a topologia da rede social é fortemente influenciada pelos assuntos falados nela. Apesar de nosso trabalho possuir como base um único dataset, os métodos e medidas desenvolvidos são gerais e podem ser aplicados a qualquer processo de difusão de informação e a qualquer rede complexa.
The raise in the availability of data regarding interactions between people online has opened new doors to study the process of information diffusion in social networks. In this present work, we make use of the data from the micro-blogging website Twitteralong with complex networks concepts to understand, characterize and classify information diffusion processes observed in this platform and in social networks in general. We present important measures to characterize information cascades and efficient algorithms to calculate them. With the help of these measures, we show that it is possible to quantify the influence of the social network in the process of information diffusion. After that, we show that information does tend to travel along shortest paths on Twitter. Finally, we show that the topology of the social network, without any extra semantic information, can be used to aggregate topics, and that such topology is highly influenced by the topics being discussed on it. Altough we work with only a single dataset, our methods and measures developed are general and can be applied to any process of information diffusion and any complex network.

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Fajardo, Fontiveros Oscar. "Transitions in Bayesian model selection problems: Network-based recommender system and symbolic regression". Doctoral thesis, Universitat Rovira i Virgili, 2021. http://hdl.handle.net/10803/673178.

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En aquesta tesi hem fet un estudi del procés d'inferència Bayesiana aplicat als problemes de selecció de models. Aquests problemes consisteixen en donat un conjunt de models i unes dades observades, mirar quin és es model més plausible. Fent servir el teorema de Bayes en aquest tipus de problemes, ens permet no només valorar com un model s'ajusta a ses dades amb la funció de verosimilitud, sinó que també té en compte com sa informació que pensem que a priori pugui ser certa amb el prior. Açò fa que els tipus de prediccions que obtenim d'aquests processos puguin estar basats en ses dades, bé en ses hipòtesis que tenim, o bé per ambdues. S'objectiu d'aquesta tesi és veure com afecten els diferents tipus de models trobats ens ses prediccions, per açò hem decidit enfocar-nos en dos problemes: es problema d'es sistema de recomanacions i es problema de regressió simbòlica. Es sistema de recomanacions és un problema que consisteix en predir ses preferències d'un usuari donat ses que ja sabem. En aquest cas hem fet servir un mètode bayesià i hem posat com a prior que els usuaris amb atributs semblants tinguin gustos semblants. Es es cas de sa regressió simbòlica, es problema consisteix en trobar sa expressió matemàtica de entre tot s'espai d'expresions matemàtiques. Aquí hem fet servir es Bayesian machine scientists que fa servir es teorema de Bayes i té com a prior que ses expressions matemàtiques siguen semblants als que hi ha a sa Wikipèdia. Com a resultat, hem vist que en diferents situacions, el prior i les dades poden ajudar o no a fer prediccions donant a lloc transicions en la predictibilitat (en el cas del recomanador) i la detectabilitat (en el cas de la regressió simbòlica).
En esta tesis hemos hecho un estudio del proceso de inferencia Bayesiana aplicado a los problemas de selección de modelos. Estos problemas consisten en dado un conjunto de modelos y unos datos observados, mirar cuál es se modelo más plausible. Usando el teorema de Bayes en este tipo de problemas, nos permite no sólo valorar cómo un modelo se ajusta a sus datos con la función de verosimilitud, sino que también tiene en cuenta la información que pensamos a priori pueda ser cierta con el prior. Esto hace que los tipos de predicciones que obtenemos de estos procesos puedan estar basados en sus datos, bien en las hipótesis que tenemos a priori, o bien por ambas. El objetivo de esta tesis es ver cómo afectan los diferentes tipos de modelos encontrados en las predicciones, por esto hemos decidido enfocarnos en dos problemas: el problema del sistema de recomendaciones y el problema de regresión simbólica. El sistema de recomendaciones es un problema que consiste al predecir sus preferencias de un usuario dado sus que ya sabemos. En este caso hemos usado un método bayesiano y hemos puesto como prior que los usuarios con atributos parecidos tengan gustos parecidos. En el problema de su regresión simbólica, el problema consiste en encontrar la expresión matemática de entre todo un espacio de expresiones matemáticas. Aquí hemos usado el Bayesian machine scientists que usa el teorema de Bayes y tiene como prior que sus expresiones matemáticas sean parecidas a las que hay en la Wikipèdia. Como resultado, hemos visto que en diferentes situaciones, el prior y los datos pueden ayudar o no a hacer predicciones dando a lugar transiciones en la predictibilidad (en el caso del recomanador) y a la detectabilidad (en el caso de la regresión simbólica).
In this thesis we have done a study of the Bayesian inference process applied to model selection problems. These problems consist of a set of models and observed data, looking at which model is the most plausible. Using Bayes theorem in such problems, it allows us not only to value how a model fits in with the data with the likelihood, but also to consider the information that we think can be true a priori thanks to the prior. This makes that the models that we get from these processes can be data based, based in the hypotheses we have (prior), or on both. The aim of this thesis is to see how the different types of models found affect us in the predictions, so we have decided to focus on two problems: the problem of the recommender system and the problem of symbolic regression. The recommender system is a problem with predicting the preferences of a given user who we already know. In this case we have used a Bayesian method and have set as a prior to users with similar attributes having similar tastes. In the case of symbolic regression, the problem consists in finding the best mathematical expression from all the space of mathematical expressions. Here we have used the Bayesian machine scientists that he uses Bayes' theorem where its prior looks for mathematical expressions that are like those in the Wikipedia. As a result, we have seen that in different situations, the prior and data can or cannot help making predictions by giving rise to transitions in accuracy (in the case of the recommender) and detectability (in the case of symbolic regression).

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39

Martins, Rafael Franco. "Uma proposta de modelagem por agentes para o problema da herança e desigualdade econômica". Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/100/100132/tde-12042017-111602/.

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Nos últimos anos a preocupação com uma possível elevação da desigualdade social aumentou consideravelmente, principalmente por conta da obra de Piketty intitulada O Capital no século XXI. Para Piketty a diminuição da natalidade entre os ricos será um dos pontos cruciais para este aumento. As previsões contidas no livro são avassaladoras, se nada for feito a desigualdade aumentará a ponto de ameaçar a democracia e os sistemas políticos e sociais vigentes. Porém, alguns economistas, como por exemplo: Matthew Rognlie, não concordam com esta tese e apontam supostos erros e falhas cometidos pelo economista francês. O intuito deste trabalho é tentar um caminho diferente do trilhado por Piketty, utilizando outros meios e técnicas, este trabalho se propõem a analisar a questão da desigualdade causada pela herança dos bens. Para tanto, foi implementado e adaptado o algoritmo descrito no artigo A family-network model for wealth distribution in societies para analisar esta questão à luz de técnicas e métodos como: monte carlo, grafos e simulações computacionais. O resultado obtido vai ao encontro dos resultados obtidos por Piketty
In recent years a concern for a possible rise in social inequality has increased considerably, especially on account of Piketty\'s work entitled \"Capital in the 21st Century.\" For a small reduction in the birth rate among the rich, it will be one of the crucial points for this increase. As predictions contained in the book are overwhelming, there is nothing for an inequality will increase a threatening point to democracy and existing political and social systems. However, some economists, such as Matthew Rognlie, do not agree with this article and point out supposed errors and failures committed by the French economist. The purpose of this paper is to try a different path from Piketty, using other means and techniques, this work is useful for an analysis of the issue of inequality caused by the inheritance of goods. A model of family network for the distribution of wealth in societies \"to a model of family network for the distribution of wealth in societies. The result obtained is in line with the results obtained by Piketty

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Ferreira, Leandro Augusto. "As redes complexas e o estudo do risco sistêmico no sistema financeiro". Universidade de São Paulo, 2013. http://www.teses.usp.br/teses/disponiveis/100/100132/tde-12092013-184127/.

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As crises financeiras são processos de perdas decorrentes do mecanismo do mercado financeiro. Elas afetam as instituições do sistema financeiro e por meio do processo de contágio se espalham por ele, algumas vezes analogamente ao efeito dominó. Este processo pode levar muitas instituições financeiras saudáveis a se tornarem insolventes. Isso acontece porque os agentes econômicos estão interligados por meio de relações contratuais e se tornam dependentes uns aos outros. O risco sistêmico pode ser entendido como o risco de uma grande perda em um sistema. O presente trabalho tem como objetivo utilizar as propriedades de um modelo de contágio, proposto para estudar os efeitos da propagação de crises financeiras, bem como a mensuração do risco sistêmico no sistema interbancário. Este problema foi investigado considerando três diferentes topologias de rede: Erdös-Rényi, Livre de Escala (ou Scale-Free) e Interbancária Empírica. A escolha destas topologias foi pelo fato de que duas delas - Livre de Escala e Interbancária Empírica - podem emular o sistema bancário real e a de Erdös-Rényi ter sido utilizada em diversos modelos da literatura. Cada nó representa um banco que possui balanço patrimonial constituído de passivos (patrimônio líquido, empréstimos e depósitos) e ativos (empréstimos, títulos e valores mobiliários). Foi analisada a influência da alavancagem do sistema, da probabilidade inicial de default e do número de clusters da rede Interbancária Empírica. O risco sistêmico foi medido utilizando o Indicador de Risco Sistêmico, o Índice de Risco Sistêmico e o VaR Sistêmico. Mostrou-se que as redes Livres de Escala são mais robustas em relação aos ataques aleatórios evitando o aumento da inadimplência. O aumento abrupto do impacto causados pela crise acontece devido ao aumento do grau de alavancagem do sistema. O número de clusters da rede Interbancária Empírica impacta a robustez do sistema. O modelo reproduz o resultado conhecido como Muito Interconectado para Falhar, que é quando bancos mais interconectados oferecem maior risco ao sistema.
The financial crises are processes of losses arising from financial market mechanism. They affect the institutions of the financial system by the process of contagion. Sometimes it is equal to the domino effect. This process can make many healthy financial institutions become insolvents. It happens because economic agents are interconnected through contractual relations and become dependent on each other. Systemic risk can be understood as the risk of a huge loss in a system. The present work aims to study the properties of a contagion model proposed to study the effects of the spread of financial crises, as well as the measurement of systemic risk in the interbank system. This problem was investigated considering three different network topologies: Erdös-Rényi, Scale-Free and Empirical Interbank. The choice of these topologies was made by the fact that two of them - Scale-Free and Empirical Interbank - may emulate the real banking system and Erdös-Rényi has been used in several models in the literature. Each node is a bank and consists on a balance sheet split as liabilities (equity, borrowings and deposits) and assets (lendings, bonds and securities). It was analyzed the influence of the coefficient of leverage, the influence of the initial probability of default and the influence of the number of clusters on the Empirical Interbank. The systemic risk was measured using the Systemic Risk Indicator, Systemic Index and Systemic Value at Risk. It was shown that Scale-Free networks are more robust against random attacks, avoiding increases in the number of defaults. The abrupt increase in the impact caused by the crisis happens due to the increase in coefficient of leverage. The number of clusters on Empirical Interbank network impacts the robustness of the system. The model reproduces the result known as Too Interconnected to Fail, that is, banks more interconnected offer higher risk to the system.

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41

Lima, Nicholas Veloso. "Difusão competitiva de produtos e inovações: um modelo de duopólio em redes complexas do tipo small world". Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/100/100132/tde-30032016-081314/.

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Nos últimos 60 anos, os modelos de difusão de produtos e de inovações tiveram penetração tão ampla nos mais diversos campos de investigação científica que se tornaram ubíquos, sendo empregados em contextos diversos como no marketing, na Medicina, na Antropologia, na Geografia, por exemplo. Essa abrangência é devido ao papel vital que produtos, inovações e novas tecnologias têm na vida dos indivíduos e no impacto que exercem nas dinâmicas e no desenvolvimento de comunidades, países e de suas economias. Porém, após os grandes saltos dados nas décadas de 1960 e 1970, os estudos em difusão de bens de consumo duráveis deram lugar a pesquisas em sistemas de inovação nas duas décadas seguintes, só voltando a gerar maior interesse acadêmico a partir da década de 2000, com o surgimento dos sistemas de Gestão de Relacionamento com Clientes Customer Relationship Management (CRM) , que tornou disponível um enorme volume de dados; e, também, com o desenvolvimento de novas técnicas de análise, como a modelagem de sistemas complexos. Tendo em vista a carência de estudos integrando modelos de difusão competitiva com modelos de redes usando topologias de redes parcialmente conectadas (small world e livres de escala), este estudo tem como objetivo geral caracterizar a dinâmica da difusão competitiva proposta em redes small world do tipo Watts-Strogatz. Foram realizadas simulações tanto da formulação clássica do modelo de difusão de produtos e de inovações, proposto por Bass (1969), como de proposições mais modernas para difusão competitiva, como os propostos por Libai, Muller e Peres (2009a; 2009b; 2009c) e por Peres, Muller e Mahajan (2010), além de desenvolver um novo modelo incorporando ao de Libai, Muller e Peres (2009c) a topologia de redes de pequeno mundo e outras características de difusão competitiva não presentes na formulação original , permitindo fazer inferências sobre o comportamento da difusão em diversos cenários que não são explicitamente previstos nas formulações clássicas. Por sua lógica intuitiva e simples, o modelo proposto neste trabalho é de valor significativo para o ensino e para a pesquisa da difusão competitiva
In the last 60 years, product and innovation models were so widespread in so many fields of study that they became ubiquitous, being employed in such diverse backgrounds like marketing, medicine, anthropology and geography. Such widespread influence arises from the fact that products, innovations and Technologies have a big role in any individuals daily lives and a huge impact on the development and dynamics of communities, countries and its economies. After huge leaps on this field of research during the 1960s and 1970s, its study faded away from mainstream research in the following two decades. Only regaining widespread academic interest in the beginning of 21st century, with the advent of Customer Relationship Management systems, which made available huge amounts of data, other factors that contributed to this resurgence in diffusion literature were the advancements on new tools for research, notably the developments in complex systems theory and network theory. In the view of the still small, but rapidly increasing, number of studies integrating competitive diffusion and network models of partially connected networks (such as small world networks and scale-free networks), this study aims to characterize the dynamics of competitive diffusion in small world networks with the Watts-Strogatz topology. For its intended purpose, simulations were created, both for the classical formulation of the Bass Diffusion Model, as well as more modern approaches for competitive diffusion, such as the models proposed by Libai, Muller and Peres and Peres, Muller and Mahajan. A new model was developed in order expand the model proposed by Libai et al (2009c) in order incorporate the small world network topology and other characteristics associated to competition that were not explicitly represented. Allowing the inference of behaviors in various scenarios that are not explicitly covered in the classical formulations. For intuitive logic and simplicity, it is believed that this model is of significant value for teaching and for the study of competitive diffusion

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42

Arruda, Guilherme Ferraz de. "Mineração de dados em redes complexas: estrutura e dinâmica". Universidade de São Paulo, 2013. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-25062013-085958/.

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A teoria das redes complexas é uma área altamente interdisciplinar que oferece recursos para o estudo dos mais variados tipos de sistemas complexos, desde o cérebro até a sociedade. Muitos problemas da natureza podem ser modelados como redes, tais como: as interações protéicas, organizações sociais, o mercado financeiro, a Internet e a World Wide Web. A organização de todos esses sistemas complexos pode ser representada por grafos, isto é, vértices conectados por arestas. Tais topologias têm uma influencia fundamental sobre muitos processos dinâmicos. Por exemplo, roteadores altamente conectados são fundamentais para manter o tráfego na Internet, enquanto pessoas que possuem um grande número de contatos sociais podem contaminar um grande número de outros indivíduos. Ao mesmo tempo, estudos têm mostrado que a estrutura do cérebro esta relacionada com doenças neurológicas, como a epilepsia, que está ligada a fenômenos de sincronização. Nesse trabalho, apresentamos como técnicas de mineração de dados podem ser usadas para estudar a relação entre topologias de redes complexas e processos dinâmicos. Tal estudo será realizado com a simulação de fenômenos de sincronização, falhas, ataques e propagação de epidemias. A estrutura das redes será caracterizada através de métodos de mineração de dados, que permitirão classificar redes de acordo com um conjunto de modelos e determinar padrões de conexões presentes na organização de diferentes tipos de sistemas complexos. As análises serão realizadas com aplicações em neurociências, biologia de sistemas, redes sociais e Internet
The theory of complex networks is a highly interdisciplinary reseach area offering resources for the study of various types of complex systems, from the brain to the society. Many problems of nature can be modeled as networks, such as protein interactions, social organizations, the financial market, the Internet and World Wide Web. The organization of all these complex systems can be represented by graphs, i.e. a set of vertices connected by edges. Such topologies have a fundamental influence on many dynamic processes. For example, highly connected routers are essential to keep traffic on the Internet, while people who have a large number of social contacts may infect many other individuals. Indeed, studies have shown that the structure of brain is related to neurological conditions such as epilepsy, which is relatad to synchronization phenomena. In this text, we present how data mining techniques data can be used to study the relation between complex network topologies and dynamic processes. This study will be conducted with the simulation of synchronization, failures, attacks and the epidemics spreading. The structure of the networks will be characterized by data mining methods, which allow classifying according to a set of theoretical models and to determine patterns of connections present in the organization of different types of complex systems. The analyzes will be performed with applications in neuroscience, systems biology, social networks and the Internet

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43

Aldecoa, García Rodrigo. "Detección de comunidades en redes complejas". Doctoral thesis, Universitat Politècnica de València, 2013. http://hdl.handle.net/10251/31638.

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El uso de las redes para modelar sistemas complejos es creciente en multitud de ambitos. Son extremadamente utiles para representar interacciones entre genes, relaciones sociales, intercambio de informaci on en Internet o correlaciones entre precios de acciones burs atiles, por nombrar s olo algunos ejemplos. Analizando la estructura de estas redes, comprendiendo c omo interaccionan sus distintos elementos, podremos entender mejor c omo se comporta el sistema en su conjunto. A menudo, los nodos que conforman estas redes tienden a formar grupos altamente conectados. Esta propiedad es conocida como estructura de comunidades y esta tesis doctoral se ha centrado en el problema de c omo mejorar su detecci on y caracterizaci on. Como primer objetivo de este trabajo, se encuentra la generaci on de m etodos e cientes que permitan caracterizar las comunidades de una red y comprender su estructura. Segundo, pretendemos plantear una serie de pruebas donde testar dichos m etodos. Por ultimo, sugeriremos una medida estad stica que pretende ser capaz de evaluar correctamente la calidad de la estructura de comunidades de una red. Para llevar a cabo dichos objetivos, en primer lugar, se generan una serie de algoritmos capaces de transformar una red en un arbol jer arquico y, a partir de ah , determinar las comunidades que aparecen en ella. Por otro lado, se ha dise~nado un nuevo tipo de benchmarks para testar estos y otros algoritmos de detecci on de comunidades de forma e ciente. Por ultimo, y como parte m as importante de este trabajo, se demuestra que la estructura de comunidades de una red puede ser correctamente evaluada utilizando una medida basada en una distribuci on hipergeom etrica. Por tanto, la maximizaci on de este ndice, llamado Surprise, aparece como la estrategia id onea para obtener la partici on en comunidades optima de una red. Surprise ha mostrado un comportamiento excelente en todos los casos analizados, superando cualitativamente a cualquier otro m etodo anterior. De esta manera, aparece como la mejor medida propuesta para este n y los datos sugieren que podr a ser una estrategia optima para determinar la calidad de la estructura de comunidades en redes complejas.
Aldecoa García, R. (2013). Detección de comunidades en redes complejas [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/31638
TESIS
Premiado

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44

RADICIONI, Tommaso. "All the ties that bind. A socio-semantic network analysis of Twitter political discussions". Doctoral thesis, Scuola Normale Superiore, 2021. http://hdl.handle.net/11384/109224.

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Social media play a crucial role in what contemporary sociological reflections define as a ‘hybrid media system’. Online spaces created by social media platforms resemble global public squares hosting large-scale social networks populated by citizens, political leaders, parties and organizations, journalists, activists and institutions that establish direct interactions and exchange contents in a disintermediated fashion. In the last decade, an increasing number of studies from researchers coming from different disciplines has approached the study of the manifold facets of citizen participation in online political spaces. In most cases, these studies have focused on the investigation of direct relationships amongst political actors. Conversely, relatively less attention has been paid to the study of contents that circulate during online discussions and how their diffusion contributes to building political identities. Even more rarely, the study of social media contents has been investigated in connection with those concerning social interactions amongst online users. To fill in this gap, my thesis work proposes a methodological procedure consisting in a network-based, data-driven approach to both infer communities of users with a similar communication behavior and to extract the most prominent contents discussed within those communities. More specifically, my work focuses on Twitter, a social media platform that is widely used during political debates. Groups of users with a similar retweeting behavior - hereby referred to as discursive communities - are identified starting with the bipartite network of Twitter verified users retweeted by nonverified users. Once the discursive communities are obtained, the corresponding semantic networks are identified by considering the co-occurrences of the hashtags that are present in the tweets sent by their members. The identification of discursive communities and the study of the related semantic networks represent the starting point for exploring more in detail two specific conversations that took place in the Italian Twittersphere: the former occured during the electoral campaign before the 2018 Italian general elections and in the two weeks after Election day; the latter centered on the issue of migration during the period May-November 2019. Regarding the social analysis, the main result of my work is the identification of a behavior-driven picture of discursive communities induced by the retweeting activity of Twitter users, rather than determined by prior information on their political affiliation. Although these communities do not necessarily match the political orientation of their users, they are closely related to the evolution of the Italian political arena. As for the semantic analysis, this work sheds light on the symbolic dimension of partisan dynamics. Different discursive communities are, in fact, characterized by a peculiar conversational dynamics at both the daily and the monthly time-scale. From a purely methodological aspect, semantic networks have been analyzed by employing three (increasingly restrictive) benchmarks. The k-shell decomposition of both filtered and non-filtered semantic networks reveals the presence of a core-periphery structure providing information on the most debated topics within each discursive community and characterizing the communication strategy of the corresponding political coalition.

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45

Arulselvan, Ashwin. "Complex network assortment and modeling". [Gainesville, Fla.] : University of Florida, 2006. http://purl.fcla.edu/fcla/etd/UFE0014925.

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Novelli, Leonardo. "Relating network structure and function via information theory". Thesis, University of Sydney, 2020. https://hdl.handle.net/2123/24093.

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Complex systems are increasingly studied as dynamical systems unfolding on complex networks, although our current understanding of the relationship between their structure and function is far from complete. Information theory provides a useful mathematical framework and quantitative tools for bridging this gap. However, its application to large datasets has been hindered by computational, statistical, and theoretical obstacles. This thesis proposes and tests viable solutions to current challenges related to network inference from multivariate time series. The computational challenge is tackled by adapting existing efficient greedy algorithms, which produce minimal network models and mitigate the curse of dimensionality in the practical estimation of information-theoretic functionals. The multiple-comparisons problem is solved by the newly-introduced maximum statistic test, which is compatible with parallel computing and enables a validation study on 100 nodes and 10000 time samples—each one order of magnitude larger than previously demonstrated. This is the typical scale of magnetoencephalography and fMRI brain scans in neuroscience, the quintessential complex system and the main application domain discussed throughout this thesis. From a theoretical perspective, this thesis contributes the first analytical derivation of pairwise transfer entropy from network structure under the assumption of autoregressive dynamics. Beside the directed link weight, clustered network motifs and source in-degree are found responsible for enhancing the transfer entropy. The abundance of these local structures differs between network topologies and provides insights on how network structure affects the performance of inference algorithms. Such influence of the topology and motifs on the identification of links and macroscopic network properties is elucidated theoretically and numerically in the final chapter. Taken together, the advances presented in this thesis open the way for the practical application of the information-theoretic framework to studying real complex systems at a relevant scale.

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Patricio, Vitor Hugo Louzada. "Canalização: fenótipos robustos como consequência de características da rede de regulação gênica". Universidade de São Paulo, 2011. http://www.teses.usp.br/teses/disponiveis/95/95131/tde-30052011-223151/.

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Em sistemas biológicos, o estudo da estabilidade das redes de regulação gênica é visto como uma contribuição importante que a Matemática pode proporcionar a pesquisas sobre câncer e outras doenças genéticas. Neste trabalho, utilizamos o conceito de ``canalização\'\' como sinônimo de estabilidade em uma rede biológica. Como as características de uma rede de regulação canalizada ainda são superficialmente compreendidas, estudamos esse conceito sob o ponto de vista computacional: propomos um modelo matemático simplificado para descrever o fenômeno e realizamos algumas análises sobre o mesmo. Mais especificamente, a estabilidade da maior bacia de atração das redes Booleanas - um clássico paradigma para a modelagem de redes de regulação - é analisada. Os resultados indicam que a estabilidade da maior bacia de atração está relacionada com dados biológicos sobre o crescimento de colônias de leveduras e que considerações sobre a interação entre as funções Booleanas e a topologia da rede devem ser realizadas conjuntamente na análise de redes estáveis.
In biological systems, the study of gene regulatory networks stability is seen as an important contribution that Mathematics can make to cancer research and that of other genetic diseases. In this work, we consider the concept of ``canalization\'\' as a consequence of stability in gene regulatory networks. The characteristics of canalized regulatory networks are superficially understood. Hence, we study the canalization concept under a computational framework: a simplified model is proposed to describe the phenomenon using Boolean Networks - a classical paradigm to modeling regulatory networks. Specifically, the stability of the largest basin of attraction in gene regulatory networks is analyzed. Our results indicate that the stability of the largest basin of attraction is related to biological data on growth of yeast colonies, and that thoughts about the interaction between Boolean functions and network topologies must be given in the analysis of stable networks.

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48

Tirico, Michele. "Morphogenesis of complex networks. : An application in urban growth". Thesis, Normandie, 2020. http://www.theses.fr/2020NORMLH17.

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Les caractéristiques, les fonctionnements et les processus de morphogénèse d'un grand nombre de réseaux spatio-complexes sont influencés par la position et la géométrie de leurs éléments constitutifs. Nous abordons, dans ce travail, les aspects computationnels de la morphogénèse de réseaux complexes, en proposant un modèle général, capable de simuler leur formation. Les réseaux sont générés sous l'influence de contraintes qui s'expriment par l'intermédiaire d'un champ vectoriel qui est déterminé à l'aide d'un système de réaction-diffusion. Nous utilisons un modèle de Gray-Scott produisant une grande variété de motifs dynamiques. Le champ vectoriel obtenu contrôle la géométrie et le taux de croissance du réseau construit qui rétroagit sur le processus de réaction-diffusion. Une étude a été réalisée sur l'influence des motifs et des processus de rétroaction sur la structure des réseaux obtenus en s'appuyant sur des mesures de réseaux complexes et de multi-fractalités. Une démarche de validation et d'évaluation du comportement du modèle a été effectuée et appliquée en comparant les réseaux obtenus à ceux structurant les villes françaises les plus importantes en taille et les plus connues graphes géométriques planaires
The characteristics, functions and morphogenetic processes of a large number of complex spatial networks are influenced by the position and the geometry of their constituent elements. In this work, we address the computational aspects of the morphogenesis of complex networks by proposing a general model, simulating their formation. The networks are generated under the influence of constraints expressed through a vector field that is determined using a reaction-diffusion system. We use the Gray-Scott model to produce a wide variety of dynamic patterns. The resulting vector field controls the geometry and the growth rate of the constructed network that feeds back the reaction-diffusion process. A study was carried out on the influence of the patterns and feedback processes on the structure of the obtained networks using measures from graph theory and multi-fractality theory. A process of validation and evaluation of the model's behaviour was carried out and applied by comparing the networks obtained to largest French cities and the most relevant geometric planar graphs

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Wiedermann, Marc. "Classification of complex networks in spatial, topological and information theoretic domains". Doctoral thesis, Humboldt-Universität zu Berlin, 2018. http://dx.doi.org/10.18452/18818.

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Die Netzwerktheorie ist eine wirksame Methode, um die Struktur realer Systeme, z.B. des Klimasystems, zu beschreiben und zu klassifizieren. Der erste Teil dieser Arbeit nutzt diese Diskriminanzfähigkeit um die Ost- und Zentralpazifischen Phasen von El Niño und La Niña mittels eines Index basierend auf der Evaluation zeitlich entwickelnder Klimanetzwerke zu unterscheiden. Nach dem Studium der klimatischen Einflüsse dieser unterschiedenen Phasen verlegt die Arbeit ihren Schwerpunkt von der Klassifikation einzelner klimatischer Schichten auf den generelleren Fall interagierender Netzwerke. Hier repräsentieren die Teilnetzwerke entsprechende Variabilitäten in Ozean und Atmosphäre. Es zeigt sich, dass die Ozean-Atmosphären-Wechselwirkung einer hierarchischen Struktur folgt wobei makroskopische Netzwerkmaße einzelne Atmosphärenschichten bezüglich ihrer Wechselwirkung mit dem Ozean unterscheiden. Der zweite Teil dieser Arbeit untersucht den Einfluss der räumlichen Einbettung von Knoten auf topologische Netzwerkeigenschaften. Hierzu werden Nullmodelle eingeführt, welche zufällige Surrogate eines gegebenen Netzwerks erzeugen, sodass globale und lokale räumliche Eigenschaften erhalten bleiben. Diese Modelle erfassen die makroskopischen Eigenschaften der studierten Netzwerke besser als bisherige Standardmodelle zur Erzeugung von Zufallsnetzwerken. Abhängig von der Performanz der vorgeschlagenen Modelle können gegebene Netzwerke schlussendlich in verschiedene Klassen eingeteilt werden. Die Arbeit schließt mit einer Erweiterung der bisherigen Netzwerkklassifikatoren um eine zweidimensionale Metrik, welche Netzwerke auf Basis ihrer Komplexität unterscheidet. Es wird gezeigt, dass Netzwerke des gleichen Typs dazu neigen in individuellen Bereichen der resultierenden Komplexitäts-Entropie-Ebene zu liegen. Die eingeführte Methode ermöglicht auch die objektive Konstruktion von Klimanetzwerken indem Schwellwerte gewählt werden, die die statistische Komplexität maximieren.
Complex network theory provides a powerful tool to quantify and classify the structure of many real-world complex systems, including the climate system. In its first part, this work demonstrates the discriminative power of complex network theory to objectively classify Eastern and Central Pacific phases of El Niño and La Niña by proposing an index based on evolving climate networks. After an investigation of the climatic impacts of these discriminated flavors, this work moves from the classification of sets of single-layer networks to the more general study of interacting networks. Here, subnetworks represent oceanic and atmospheric variability. It is revealed that the ocean-to-atmosphere interaction in the Northern hemisphere follows a hierarchical structure and macroscopic network characteristics discriminate well different parts of the atmosphere with respect to their interaction with the ocean. The second part of this work assesses the effect of the nodes’ spatial embedding on the networks’ topological characteristics. A hierarchy of null models is proposed which generate random surrogates from a given network such that global and local statistics associated with the spatial embedding are preserved. The proposed models capture macroscopic properties of the studied spatial networks much better than standard random network models. Depending on the models’ actual performance networks can ultimately be categorized into different classes. This thesis closes with extending the zoo of network classifiers by a two-fold metric to discriminate different classes of networks based on assessing their complexity. Within this framework networks of the same category tend to cluster in distinct areas of the complexity-entropy plane. The proposed framework further allows to objectively construct climate networks such that the statistical network complexity is maximized.

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Schultz, Paul. "Stability Concepts of Networked Infrastructure Networks". Doctoral thesis, Humboldt-Universität zu Berlin, 2018. http://dx.doi.org/10.18452/19310.

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Aktuell unterliegt unsere Stromversorgung mit der Energiewende einer Transformation, welche letzten Endes auch Änderungen der Struktur des Stromnetzes bedingt. Jenes ist ein hochkomplexes System aus unzähligen Erzeugern und Verbrauchern die miteinander wechselwirken. Im Lichte dessen leiten sich, (nicht nur) für zukünftige Stromnetze, einige methodischen Fragen ab. Wie kann die Stabilität verschiedener Betriebszustände oder Szenarien miteinander verglichen werdem? Welches sind die neuralgischen Punkte eines Stromnetzes? Zu welchem Grad bestimmt die Netzwerkstruktur die Systemstabilität? Im Zentrum der vorliegenden Dissertation steht dabei das emergente Phänomen der Synchronisation in Oszillatornetzwerken sowie dessen Stabilität. Im Bezug auf Stromnetze ist die Synchronisation dadurch gekennzeichnet, dass alle Erzeuger und Verbraucher mit der Netzfrequenz im Takt schwingen. Mit probabilistischen Stabilitätsmaßen lässt sich die Systemstabilität auf verschiedene Art quantifizieren. Neben einer Untersuchung möglicher Beschränkungen werden zwei neue probabilistische Maße entwickelt. Dabei spielen insbesondere die Häufigkeit und Dauer von Störungen sowie die Einhaltung der Betriebsgrenzen eine Rolle. Weiterhin wird der Einfluss kleiner Netzwerkstrukturen, sogenannter Motive, auf die Stabilität herausgearbeitet. Hierzu werden die Stabilitätsmaße in statistischen Verfahren mit charakteristischen Größen aus der Netzwerktheorie verknüpft. Es zeigt sich dann, dass das Auftreten spezieller Motive die Systemstabilität erhöht, wohingegen andere diese herabsetzen. Diese Zusammenhänge zwischen Netzwerkmotiven und Stabilität der Synchronisation erweitern die Kenntnisse über Zusammenhänge zwischen Struktur und Stabilität komplexer Systeme. Darüber hinaus erweitern die neu entwickelten probabilistischen Stabilitätsmaße das Methodenspektrum der nichtlinearen Dynamik zur Stabilitätsanalyse, insbesondere für Systeme auf komplexen Netzwerken mit vielen Freiheitsgraden.
In the light of the energy transition, power systems undergo a major transformation enabled by appropriate modifications of the grid’s underlying structure. This network constitutes the complex interaction of numerous producers and consumers. The power grid is additionally subject to intermittent disturbances that also include large deviations. These aspects prompt methodological problems for (future) power grids in particular and complex systems in general. How can the stability of different operating points or scenarios be compared? What are the critical components of the network? To which extent is the stability of an operating point determined by the network structure? This dissertation focusses on the emergent phenomenon of synchronisation on networks. In power grids, this corresponds to all units working at the same rhythm – the rated grid frequency. Regarding an analysis with so-called probabilistic stability measures, important limitations are discussed and novel approaches are developed. In particular, the new measures consider repeated perturbations as well as operational bounds on transient deviations. Moreover, the influence of small network structures, so-called motifs, on the stability is investigated. For this purpose, the stability measures are paired with network characteristics using statistical approaches. On this basis, it turns out that, while the abundance of special motifs enhances stability, others typically diminish it. In conclusion, the development of analysis methods and their comparison with network characteristics uncovers relationships between network motifs and the stability of synchronisation. These results are general to a large class of complex systems and build a foundation to future research in this direction. In addition to that, the novel probabilistic stability measures extend the range of methods in nonlinear dynamics by important aspects, especially for high-dimensional complex systems.

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