Academic literature on the topic 'COMPLES NETWORK'
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Journal articles on the topic "COMPLES NETWORK"
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PELLEGRINI, Lilla, Monica LEBA, and Alexandru IOVANOVICI. "CHARACTERIZATION OF URBAN TRANSPORTATION NETWORKS USING NETWORK MOTIFS." Acta Electrotechnica et Informatica 20, no. 4 (January 21, 2020): 3–9. http://dx.doi.org/10.15546/aeei-2020-0019.
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We use tools and techniques specific to the field of complex networks analysis for the identification and extraction of key parameters which define ”good” patterns and practices for designing public transportation networks. Using network motifs we analyze a set of 18 cities using public data sets regarding the topology of network and discuss each of the identified motifs using the concepts and tools of urban planning.
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Tarapata, Zbigniew. "Modelling and analysis of transportation networks using complex networks: Poland case study." Archives of Transport 36, no. 4 (December 31, 2015): 55–65. http://dx.doi.org/10.5604/08669546.1185207.
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In the paper a theoretical bases and empirical results deal with analysis and modelling of transportation networks in Poland using complex networks have been presented. Properties of complex networks (Scale Free and Small World) and network's characteristic measures have been described. In this context, results of empirical researches connected with characteristics of passenger air links network, express railway links network (EuroCity and InterCity) and expressways/highways network in Poland have been given. For passenger air links network in Poland results are compared with the same networks in USA, China, India, Italy and Spain. In the conclusion some suggestions, observations and perspective dealing with complex network in transportation networks have been presented.
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Asbaş, Caner, Zühal Şenyuva, and Şule Tuzlukaya. "New Organizations in Complex Networks: Survival and Success." Central European Management Journal 30, no. 1 (March 15, 2022): 11–39. http://dx.doi.org/10.7206/cemj.2658-0845.68.
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Purpose: The present study investigates the survival and success of new organizations in the light of complex network theory. Methodology: The empirical data was collected using the survey method from the technology park companies are analyzed with social network analysis. Two main methods were used in this study: descriptive statistics and social network analysis. Findings: The findings indicate that new nodes appearing because of splitting up of bigger nodes from present or other related networks have a higher degree of centrality. In practice, this means that companies founded by former members of large-scale companies from these networks are more successful due to the ease in providing the flow of resources and information through previous links. This suggests that the imprint effect can be observed in the appearance, lifecycle, and performance of new nodes in complex networks. Originality: The literature lacks studies on new organizations’ lifecycle in complex networks despite the existence of studies about new organizations in organizational networks. This study examines the appearance, success, and survival of new organizations in networks by complex network approaches such as dynamism, dissipative structures, and uncertainties.
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Hu, Ziping, Krishnaiyan Thulasiraman, and Pramode K. Verma. "Complex Networks: Traffic Dynamics, Network Performance, and Network Structure." American Journal of Operations Research 03, no. 01 (2013): 187–95. http://dx.doi.org/10.4236/ajor.2013.31a018.
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Maciá-Pérez, Francisco, Iren Lorenzo-Fonseca, Jose Vicente Berná-Martinez, and Jose Manuel Sánchez-Bernabeu. "Conceptual Modelling of Complex Network Management Systems." Journal of Computers 10, no. 5 (2015): 309–20. http://dx.doi.org/10.17706/jcp.10.5.309-320.
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Xu, Shuai, and Bai Da Zhang. "Complex Network Model and its Application." Advanced Materials Research 791-793 (September 2013): 1589–92. http://dx.doi.org/10.4028/www.scientific.net/amr.791-793.1589.
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Human life is in a complex network world. In everyday life, the network can be a physical object such as the Internet, power network, road network and neural network; can also abstract not touch, such as interpersonal networks, networks of co-operation in scientific research, product supply chain network, biological populations, networks, etc.. The topology of these networks, the statistical characteristics and the formation mechanism, and so on, has a very important significance for the efficient allocation of resources, provides various functions, as well as the stability of the network, however, due to the complexity of these networks, conventional simplified model and cannot be good solution to the above problems. The complex network and network complexity has become a hot issue in the scientific and engineering concern. This article describes a few common complex network models and its application brief.
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Hernandez, Bryan S., Patrick Vincent N. Lubenia, Matthew D. Johnston, and Jae Kyoung Kim. "A framework for deriving analytic steady states of biochemical reaction networks." PLOS Computational Biology 19, no. 4 (April 13, 2023): e1011039. http://dx.doi.org/10.1371/journal.pcbi.1011039.
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The long-term behaviors of biochemical systems are often described by their steady states. Deriving these states directly for complex networks arising from real-world applications, however, is often challenging. Recent work has consequently focused on network-based approaches. Specifically, biochemical reaction networks are transformed into weakly reversible and deficiency zero generalized networks, which allows the derivation of their analytic steady states. Identifying this transformation, however, can be challenging for large and complex networks. In this paper, we address this difficulty by breaking the complex network into smaller independent subnetworks and then transforming the subnetworks to derive the analytic steady states of each subnetwork. We show that stitching these solutions together leads to the the analytic steady states of the original network. To facilitate this process, we develop a user-friendly and publicly available package, COMPILES (COMPutIng anaLytic stEady States). With COMPILES, we can easily test the presence of bistability of a CRISPRi toggle switch model, which was previously investigated via tremendous number of numerical simulations and within a limited range of parameters. Furthermore, COMPILES can be used to identify absolute concentration robustness (ACR), the property of a system that maintains the concentration of particular species at a steady state regardless of any initial concentrations. Specifically, our approach completely identifies all the species with and without ACR in a complex insulin model. Our method provides an effective approach to analyzing and understanding complex biochemical systems.
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Guo, Dong Wei, Xiang Yan Meng, and Cai Fang Hou. "Building Complex Network Similar to Facebook." Applied Mechanics and Materials 513-517 (February 2014): 909–13. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.909.
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Social networks have been developed rapidly, especially for Facebook which is very popular with 10 billion users. It is a considerable significant job to build complex network similar to Facebook. There are many modeling methods of complex networks but which cant describe characteristics similar to Facebook. This paper provide a building method of complex networks with tunable clustering coefficient and community strength based on BA network model to imitate Facebook. The strategies of edge adding based on link-via-triangular, link-via-BA and link-via-type are used to build a complex network with tunable clustering coefficient and community strength. Under different parameters, statistical properties of the complex network model are analyzed. The differences and similarities are studied among complex network model proposed by this paper and real social network on Facebook. It is found that the network characteristics of the network model and real social network on Facebook are similar under some specific parameters. It is proved that the building method of complex networks is feasible.
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Koam, Ali N. A., Ali Ahmad, and Yasir Ahmad. "Computation of reverse degree-based topological indices of hex-derived networks." AIMS Mathematics 6, no. 10 (2021): 11330–45. http://dx.doi.org/10.3934/math.2021658.
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<abstract><p>Network theory gives an approach to show huge and complex frameworks through a complete arrangement of logical devices. A network is made is made of vertices and edges, where the degree of a vertex refers to the number of joined edges. The degree appropriation of a network represents the likelihood of every vertex having a particular degree and shows significant worldwide network properties. Network theory has applications in many disciplines like basic sciences, computer science, engineering, medical, business, public health and sociology. There are some important networks like logistical networks, gene regulatory networks, metabolic networks, social networks, derived networks. Topological index is a numerical number assigned to the molecular structure/netwrok which is used for correlation analysis in physical, theoretical and environmental chemistry. The hex-derived networks are created by hexagonal networks of dimension $ t $, these networks have an assortment of valuable applications in computer science, medical science and engineering. In this paper we discuss the reverse degree-based topological for third type of hex-derived networks.</p></abstract>
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Sivakumar, B., and F. M. Woldemeskel. "Complex networks for streamflow dynamics." Hydrology and Earth System Sciences 18, no. 11 (November 20, 2014): 4565–78. http://dx.doi.org/10.5194/hess-18-4565-2014.
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Abstract. Streamflow modeling is an enormously challenging problem, due to the complex and nonlinear interactions between climate inputs and landscape characteristics over a wide range of spatial and temporal scales. A basic idea in streamflow studies is to establish connections that generally exist, but attempts to identify such connections are largely dictated by the problem at hand and the system components in place. While numerous approaches have been proposed in the literature, our understanding of these connections remains far from adequate. The present study introduces the theory of networks, in particular complex networks, to examine the connections in streamflow dynamics, with a particular focus on spatial connections. Monthly streamflow data observed over a period of 52 years from a large network of 639 monitoring stations in the contiguous US are studied. The connections in this streamflow network are examined primarily using the concept of clustering coefficient, which is a measure of local density and quantifies the network's tendency to cluster. The clustering coefficient analysis is performed with several different threshold levels, which are based on correlations in streamflow data between the stations. The clustering coefficient values of the 639 stations are used to obtain important information about the connections in the network and their extent, similarity, and differences between stations/regions, and the influence of thresholds. The relationship of the clustering coefficient with the number of links/actual links in the network and the number of neighbors is also addressed. The results clearly indicate the usefulness of the network-based approach for examining connections in streamflow, with important implications for interpolation and extrapolation, classification of catchments, and predictions in ungaged basins.
Dissertations / Theses on the topic "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 TecnolÃgicoEste 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.Submitted by Edvander Pires (edvanderpires@gmail.com) on 2015-06-16T20:25:53Z No. of bitstreams: 1 2009_dis_sdsreis.pdf: 1915908 bytes, checksum: f38f094759742ce9d7cc35ad2d50209c (MD5)
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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 AguiarDissertação (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.
Books on the topic "COMPLES NETWORK"
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da F. Costa, Luciano, Alexandre Evsukoff, Giuseppe Mangioni, and Ronaldo Menezes, eds. Complex Networks. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25501-4.
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Menezes, Ronaldo, Alexandre Evsukoff, and Marta C. González, eds. Complex Networks. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-30287-9.
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Fortunato, Santo, Giuseppe Mangioni, Ronaldo Menezes, and Vincenzo Nicosia, eds. Complex Networks. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01206-8.
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Ben-Naim, Eli, Hans Frauenfelder, and Zoltan Toroczkai, eds. Complex Networks. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/b98716.
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Menezes, Ronaldo. Complex Networks. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.
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service), SpringerLink (Online, ed. Valuation of Network Effects in Software Markets: A Complex Networks Approach. Heidelberg: Springer-Verlag Berlin Heidelberg, 2010.
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Teixeira, Andreia Sofia, Diogo Pacheco, Marcos Oliveira, Hugo Barbosa, Bruno Gonçalves, and Ronaldo Menezes, eds. Complex Networks XII. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81854-8.
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Contucci, Pierluigi, Ronaldo Menezes, Andrea Omicini, and Julia Poncela-Casasnovas, eds. Complex Networks V. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05401-8.
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Cornelius, Sean P., Clara Granell Martorell, Jesús Gómez-Gardeñes, and Bruno Gonçalves, eds. Complex Networks X. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-14459-3.
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Ghoshal, Gourab, Julia Poncela-Casasnovas, and Robert Tolksdorf, eds. Complex Networks IV. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36844-8.
Full textBook chapters on the topic "COMPLES NETWORK"
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Ivanov, Plamen Ch, and Ronny P. Bartsch. "Network Physiology: Mapping Interactions Between Networks of Physiologic Networks." In Understanding Complex Systems, 203–22. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-03518-5_10.
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Slingerland, Willeke. "Social Capital, Corrupt Networks, and Network Corruption." In Understanding Complex Systems, 9–27. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81484-7_2.
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Kuikka, Vesa. "Subsystem Cooperation in Complex Networks - Case Brain Network." In Complex Networks XI, 156–69. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-40943-2_14.
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Li, Deyi. "Complex Networks and Network Data Mining." In Database Systems for Advanced Applications, 3. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11408079_3.
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Kenett, Dror Y., Jianxi Gao, Xuqing Huang, Shuai Shao, Irena Vodenska, Sergey V. Buldyrev, Gerald Paul, H. Eugene Stanley, and Shlomo Havlin. "Network of Interdependent Networks: Overview of Theory and Applications." In Understanding Complex Systems, 3–36. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-03518-5_1.
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Adiga, Abhijin, Hilton Galyean, Chris J. Kuhlman, Michael Levet, Henning S. Mortveit, and Sichao Wu. "Network Structure and Activity in Boolean Networks." In Cellular Automata and Discrete Complex Systems, 210–23. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-47221-7_16.
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Silva, Miguel E. P., Pedro Paredes, and Pedro Ribeiro. "Network Motifs Detection Using Random Networks with Prescribed Subgraph Frequencies." In Complex Networks VIII, 17–29. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54241-6_2.
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Melançon, Guy, Benjamin Renoust, and Haolin Ren. "Handling Complex Multilayer Networks—An Approach Based on Visual Network Analytics." In Understanding Complex Systems, 51–70. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-59302-5_3.
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Ginsparg, Paul. "Scholarly Information Network." In Complex Networks, 313–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-44485-5_15.
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Kamiński, Bogumił, Paweł Prałat, and François Théberge. "Network Robustness." In Mining Complex Networks, 239–50. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003218869-10.
Full textConference papers on the topic "COMPLES NETWORK"
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Semenets, Valerii, Valeriia Chumak, Iryna Svyd, Oleg Zubkov, Oleksandr Vorgul, and Natalia Boiko. "DESIGNING THE STRUCTURE OF A GENERAL-PURPOSE TELEMEDICINE COMPLEX." In 2021 III International Scientific and Practical Conference Theoretical and Applied Aspects of Device Development on Microcontrollers and FPGAs. MC-ampFPGA-2021, 2021. http://dx.doi.org/10.35598/mcfpga.2021.016.
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Abstract Taking into account modern trends, the analysis of the construction principles of telemadic systems, networks and complexes is presented in the work. The general structure of providing telemedicine services is developed. Structures of realization of portable and mobile telemedicine complexes are offered. Infocommunication systems and networks for implementation in mobile telemedicine complexes are analyzed. The advantages of using the sensor mobile body area network of the IEEE 802.15.6 WBAN standard in mobile telemedicine complexes are justified. Keywords: telemedicine, telemedicine complex, medical services, device structure, microcontroller, sensor network, wireless sensor body area network.
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Sha, Zhenghui, and Jitesh H. Panchal. "A Degree-Based Decision-Centric Model for Complex Networked Systems." In ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/detc2016-60036.
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The system-level structure and performance of complex networked systems (e.g., the Internet) are emergent outcomes resulting from the interactions among individual entities (e.g., the autonomous systems in the Internet). Thus, the systems evolve in a bottom-up manner. In our previous studies, we have proposed a framework towards laying complex systems engineering on decision-centric foundations. In this paper, we apply that framework on modeling and analyzing the structure and performance of complex networked systems through the integration of random utility theory, continuum theory and percolation theory. Specifically, we propose a degree-based decision-centric (DBDC) network model based on random utility theory. We analyze the degree distribution and robustness of networks generated by the DBDC model using continuum theory and percolation theory, respectively. The results show that by controlling node-level preferences, the model is capable of generating a variety of network topologies. Further, the robustness of networks is observed to be highly sensitive to the nodes’ preferences to degree. The proposed decision-centric approach has two advantages: 1) it provides a more general model for modeling networked systems by considering node-level preferences, and 2) the model can be extended by including non-structural attributes of nodes. With the proposed approach, systems that are evolved in a bottom-up manner can be modeled to verify hypothesized evolution mechanisms. This helps in understanding the underlying principles governing systems evolution, which is crucial to the development of design and engineering strategies for complex networked systems.
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Haley, Brandon M., Andy Dong, and Irem Y. Tumer. "Creating Faultable Network Models of Complex Engineered Systems." In ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/detc2014-34407.
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This paper presents a new methodology for modeling complex engineered systems using complex networks for failure analysis. Many existing network-based modeling approaches for complex engineered systems “abstract away” the functional details to focus on the topological configuration of the system and thus do not provide adequate insight into system behavior. To model failures more adequately, we present two types of network representations of a complex engineered system: a uni-partite architectural network and a weighted bi-partite behavioral network. Whereas the architectural network describes physical inter-connectivity, the behavioral network represents the interaction between functions and variables in mathematical models of the system and its constituent components. The levels of abstraction for nodes in both network types affords the evaluation of failures involving morphology or behavior, respectively. The approach is shown with respect to a drivetrain model. Architectural and behavioral networks are compared with respect to the types of faults that can be described. We conclude with considerations that should be employed when modeling complex engineered systems as networks for the purpose of failure analysis.
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Yang, Chun-Lin, and C. Steve Suh. "On the Proper Description of Complex Network Dynamics." In ASME 2018 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/imece2018-88051.
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Real-world networks are dynamical complex network systems. The dynamics of a network system is a coupling of the local dynamics with the global dynamics. The local dynamics is the time-varying behaviors of ensembles at the local level. The global dynamics is the collective behavior of the ensembles following specific laws at the global level. These laws include basic physical principles and constraints. Complex networks have inherent resilience that offsets disturbance and maintains the state of the system. However, when disturbance is potent enough, network dynamics can be perturbed to a level that ensembles no longer follow the constraint conditions. As a result, the collective behavior of a complex network diminishes and the network collapses. The characteristic of a complex network is the response of the system which is time-dependent. Therefore, complex networks need to account for time-dependency and obey physical laws and constraints. Statistical mechanics is viable for the study of multi-body dynamic systems having uncertain states such as complex network systems. Statistical entropy can be used to define the distribution of the states of ensembles. The difference between the states of ensembles define the interaction between them. This interaction is known as the collective behavior. In other words statistical entropy defines the dynamics of a complex network. Variation of entropy corresponds to the variation of network dynamics and vice versa. Therefore, entropy can serve as an indicator of network dynamics. A stable network is characterized by a specific entropy while a network on the verge of collapse is characterized by another. As the collective behavior of a complex network can be described by entropy, the correlation between the statistical entropy and network dynamics is investigated.
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Kupershtein, Leonid M., Mykhailo D. Krentsin, and Andrii V. Prytula. "Use of peer-to-peer networks for secured communication." In 16th IC Measurement and Control in Complex Systems. Vinnytsia: VNTU, 2022. http://dx.doi.org/10.31649/mccs2022.20.
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Peculiarities of the applied application of peering networks are considered (every node is equal; it can be both a client and a server). Mathematically, a decentralized network can be represented as a graph. The reasons for the emergence of peering networks are analyzed (low load on the client processor, the need to provide access to data and joint work on data). The classification of P2P networks according to three characteristics (functions, degree of centralization and method of connection) is also considered. The relevance and perspective of their application for personal communication, especially for data exchange within the company (required data security, fault tolerance and independence from Internet access) are determined.
A peering network organization model is proposed, which provides for increased data security (messages, files, audio, user settings, etc.), reliable authentication of nodes (based on a combination of manual key distribution and trust network principles), scalability of the network itself and expansion of its functional capabilities (sending text, files, support for audio, video, etc.). The software implementation should work on most modern devices and operating systems (Android, IOS, Windows, MacOS). The created model makes it possible to increase security due to the combination of various cryptographic algorithms and data exchange protocols. Data exchange is based on the Tox protocol, which involves the use of distributed hash tables and asymmetric encryption. Data transport using the Tox protocol is organized based on TCP and UDP. A graphic representation of the above-described model is given, as well as a key exchange scheme between nodes. The data is also stored at the node in a protected form, and it can be read only if the key is present.
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Sha, Zhenghui, and Jitesh H. Panchal. "Estimating the Node-Level Behaviors in Complex Networks From Structural Datasets." In ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/detc2013-12063.
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There is an emerging class of networks that evolve endogenously based on the local characteristics and behaviors of nodes. Examples of such networks include social, economic, and peer-to-peer communication networks. The node-level behaviors determine the overall structure and performance of these networks. This is in contrast to exogenously designed networks whose structures are directly determined by network designers. To influence the performance of endogenous networks, it is crucial to understand a) what kinds of local behaviors result in the observed network structures and b) how these local behaviors influence the overall performance. The focus in this paper is on the first aspect, where information about the structure of networks is available at different points in time and the goal is to estimate the behavior of nodes that resulted in the observed structures. We use three different approaches to estimate the node-level behaviors. The first approach is based on the generalized preferential attachment model of network evolution. In the second approach, statistical regression-based models are used to estimate the node-level behaviors from consecutive snapshots of the network structure. In the third approach, the nodes are modeled as rational decision-making agents who make linking decisions based on the maximization of their payoffs. Within the decision-making framework, the multinomial logit choice model is adopted to estimate the preferences of decision-making nodes. The autonomous system (AS) level Internet is used as an illustrative example to illustrate and compare the three approaches.
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Louri, Ahmed, Hongki Sung, Yoonkeon Moon, and Bernard P. Zeigler. "An Efficient Signal Distinction Scheme for Large-scale Free-space Optical Networks Using Genetic Algorithms." In Photonics in Switching. Washington, D.C.: Optica Publishing Group, 1995. http://dx.doi.org/10.1364/ps.1995.pthc5.
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Free-space optical interconnection networks can be classified into two types, space variant and space invariant, according to the degree of space variance [1]. The degree of space variance determines the network’s complexity and regularity. A totally space variant network allows a completely arbitrary interconnection between nodes, whereas a totally space invariant network has a definite, regular structure with all the nodes having the same connection patterns. In terms of physical implementations, the degree of space variance can be interpreted as the degree of sharing beam steering optics among the nodes of a given network. In other words, all nodes in a totally space-invariant network can share a single beam steering optics (considering multiple fanouts as one steering optics) to realize the given network topology, whereas, in a totally space variant network, each node requires a distinct beam steering optics. This is one of the reasons why space variant networks require complex optical implementations that often result in low interconnection density and high cost.
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Melo, Renato Silva, and André Luís Vignatti. "Preprocessing Rules for Target Set Selection in Complex Networks." In Brazilian Workshop on Social Network Analysis and Mining. Sociedade Brasileira de Computação, 2020. http://dx.doi.org/10.5753/brasnam.2020.11167.
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In the Target Set Selection (TSS) problem, we want to find the minimum set of individuals in a network to spread information across the entire network. This problem is NP-hard, so find good strategies to deal with it, even for a particular case, is something of interest. We introduce preprocessing rules that allow reducing the size of the input without losing the optimality of the solution when the input graph is a complex network. Such type of network has a set of topological properties that commonly occurs in graphs that model real systems. We present computational experiments with real-world complex networks and synthetic power law graphs. Our strategies do particularly well on graphs with power law degree distribution, such as several real-world complex networks. Such rules provide a notable reduction in the size of the problem and, consequently, gains in scalability.
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Lilian Huang, Meng Gao, and Guanghui Sun. "Complete synchronization in weighted complex networks." In 2010 3rd International Symposium on Systems and Control in Aeronautics and Astronautics (ISSCAA 2010). IEEE, 2010. http://dx.doi.org/10.1109/isscaa.2010.5632365.
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Lezhniuk, Petro D., and Vladyslav M. Lysyi. "Assessment of the impact of factors which influence the energy efficiency of res during the balancing of electrical energy system modes." In 16th IC Measurement and Control in Complex Systems. Vinnytsia: VNTU, 2022. http://dx.doi.org/10.31649/mccs2022.11.
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The paper evaluates the factors caused by the massive construction of renewable energy sources (RES), which in turn reduce their energy efficiency, and the significant generation capacity, which depends on weather conditions, causes difficulty in balancing the modes of electric power systems. General-purpose electrical networks are used to transmit the electricity generated by them. They affect the technical and economic parameters of electrical networks. In particular, with an increase in the amount of electricity in the balance of electric power systems (EPS).
A method of structuring electricity losses caused by power flows in electrical networks, which appear in them from green generation, is proposed, which will make it possible to increase the efficiency of RES use in mode balancing by including them in the generation schedule for the next day. As a consequence of improving the accuracy of generation forecasting, which increases the energy efficiency of such sources.
The method is based on a mathematical model of the electrical network for determining losses, in which current distribution coefficients in circuits from RES nodes and node voltages are used. As a result, a matrix of power loss distribution coefficients in lines is formed, which depends on the flow of power along the line from each circuit node. To form the array of data needed to determine losses, the steady-state mode of the electrical network is calculated or experimental measurement data is used. It is shown that the values of electricity losses in electrical networks caused by RES can be used during the operational planning of the electricity balance in the UES and, since they are addressable, their cost can be compensated accordingly.
Reports on the topic "COMPLES NETWORK"
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Soloviev, Vladimir, Victoria Solovieva, Anna Tuliakova, Alexey Hostryk, and Lukáš Pichl. Complex networks theory and precursors of financial crashes. [б. в.], October 2020. http://dx.doi.org/10.31812/123456789/4119.
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Based on the network paradigm of complexity in the work, a systematic analysis of the dynamics of the largest stock markets in the world and cryptocurrency market has been carried out. According to the algorithms of the visibility graph and recurrence plot, the daily values of stock and crypto indices are converted into a networks and multiplex networks, the spectral and topological properties of which are sensitive to the critical and crisis phenomena of the studied complex systems. This work is the first to investigate the network properties of the crypto index CCI30 and the multiplex network of key cryptocurrencies. It is shown that some of the spectral and topological characteristics can serve as measures of the complexity of the stock and crypto market, and their specific behaviour in the pre-crisis period is used as indicators- precursors of critical phenomena.
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Kleinberg, Robert D. Kleinberg Complex Networks. Fort Belvoir, VA: Defense Technical Information Center, October 2014. http://dx.doi.org/10.21236/ada612226.
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Lai, Ying-Cheng. Security of Complex Networks. Fort Belvoir, VA: Defense Technical Information Center, February 2010. http://dx.doi.org/10.21236/ada567229.
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Nechaev, V., Володимир Миколайович Соловйов, and A. Nagibas. Complex economic systems structural organization modelling. Politecnico di Torino, 2006. http://dx.doi.org/10.31812/0564/1118.
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One of the well-known results of the theory of management is the fact, that multi-stage hierarchical organization of management is unstable. Hence, the ideas expressed in a number of works by Don Tapscott on advantages of network organization of businesses over vertically integrated ones is clear. While studying the basic tendencies of business organization in the conditions of globalization, computerization and internetization of the society and the results of the financial activities of the well-known companies, the authors arrive at the conclusion, that such companies, as IBM, Boeing, Mercedes-Benz and some others companies have not been engaged in their traditional business for a long time. Their partner networks performs this function instead of them. The companies themselves perform the function of system integrators. The Tapscott’s idea finds its confirmation within the framework of a new powerful direction of the development of the modern interdisciplinary science – the theory of the complex networks (CN) [2]. CN-s are multifractal objects, the loss of multifractality being the indicator of the system transition from more complex state into more simple state. We tested the multifractal properties of the data using the wavelet transform modulus maxima approach in order to analyze scaling properties of our company. Comparative analysis of the singularity spectrumf(®), namely, the difference between maximum and minimum values of ® (∆ = ®max ¡ ®min) shows that IBM company is considerably more fractal in comparison with Apple Computer. Really, for it the value of ∆ is equal to 0.3, while for the vertically integrated company Apple it only makes 0.06 – 5 times less. The comparison of other companies shows that this dependence is of general character. Taking into consideration the fact that network organization of business has become dominant in the last 5-10 years, we carried out research for the selected companies in the earliest possible period of time which was determined by the availability of data in the Internet, or by historically later beginning of stock trade of computer companies. A singularity spectrum of the first group of companies turned out to be considerably narrower, or shifted toward the smaller values of ® in the pre-network period. The latter means that dynamic series were antipersistant. That is, these companies‘ management was rigidly controlled while the impact of market mechanisms was minimized. In the second group of companies if even the situation did changed it did not change for the better. In addition, we discuss applications to the construction of portfolios of stock that have a stable ratio of risk to return.
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Bailey, D. J. Nuclear weapons complex network management overview. Office of Scientific and Technical Information (OSTI), April 1989. http://dx.doi.org/10.2172/6295252.
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Lai, Ying C. Predicting and Controlling Complex Networks. Fort Belvoir, VA: Defense Technical Information Center, June 2015. http://dx.doi.org/10.21236/ada619238.
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Bielinskyi, Andrii O., and Vladimir N. Soloviev. Complex network precursors of crashes and critical events in the cryptocurrency market. [б. в.], December 2018. http://dx.doi.org/10.31812/123456789/2881.
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This article demonstrates the possibility of constructing indicators of critical and crash phenomena in the volatile market of cryptocurrency. For this purpose, the methods of the theory of complex networks have been used. The possibility of constructing dynamic measures of network complexity behaving in a proper way during actual pre-crash periods has been shown. This fact is used to build predictors of crashes and critical events phenomena on the examples of all the patterns recorded in the time series of the key cryptocurrency Bitcoin, the effectiveness of the proposed indicators-precursors of these falls has been identified.
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Warnick, Sean, and Daniel Zappala. Analysis and Design of Complex Network Environments. Fort Belvoir, VA: Defense Technical Information Center, February 2014. http://dx.doi.org/10.21236/ada596289.
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Warnick, Sean, and Daniel Zappala. Analysis and Design of Complex Network Environments. Fort Belvoir, VA: Defense Technical Information Center, March 2012. http://dx.doi.org/10.21236/ada557240.
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DeMar, Phil. Complex Network Analysis and Intelligent Monitoring Platform. Office of Scientific and Technical Information (OSTI), January 2018. http://dx.doi.org/10.2172/1827370.
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