Dissertations / Theses on the topic 'Complex network'
To see the other types of publications on this topic, follow the link: Complex network.
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 dissertations / theses for your research on the topic 'Complex network.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse dissertations / theses on a wide variety of disciplines and organise your bibliography correctly.
1
Lordan, Oriol. "Airline route networks : a complex network approach." Doctoral thesis, Universitat Politècnica de Catalunya, 2014. http://hdl.handle.net/10803/144526.
Full textAbstract:
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.
2
Khorramzadeh, Yasamin. "Network Reliability: Theory, Estimation, and Applications." Diss., Virginia Tech, 2015. http://hdl.handle.net/10919/64383.
Full textAbstract:
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.
3
Arulselvan, Ashwin. "Complex network assortment and modeling." [Gainesville, Fla.] : University of Florida, 2006. http://purl.fcla.edu/fcla/etd/UFE0014925.
Full text
4
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.
Full textAbstract:
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.
5
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/.
Full textAbstract:
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.
6
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.
Full text
7
Al-Musawi, Ahmad Jr. "COMPLEX NETWORK GROWING MODEL USING DOWNLINK MOTIFS." VCU Scholars Compass, 2013. http://scholarscompass.vcu.edu/etd/3088.
Full textAbstract:
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.
8
Silva, Diamantino Castanheira da. "Complex network view of envolving manifolds." Master's thesis, Universidade de Aveiro, 2016. http://hdl.handle.net/10773/21652.
Full textAbstract:
Mestrado em FísicaNeste estudo investigamos redes complexas formadas por triangulações de variedades topológicas em evolução, localmente homeomórficas a um plano. O conjunto de transformações dessas redes é restringida pela condição de que a cada passo todas as faces se mantenham triangulares. Neste trabalho adotamos duas abordagens principais. Na primeira abordagem crescemos variedades usando várias regras simples, que progressivamente adicionam novos triângulos. Na outra abordagem relaxamos a estrutura de variedades grandes, mantendo o número de triângulos constante. As redes resultantes da evolução destas triangulações demonstram várias características interessantes e inesperadas em redes planares, tais como diâmetros ”small-world” e distribuições de grau tipo lei de potência. Finalmente manipulámos a topologia das variedades pela introdução de ”wormholes”. A presença de ”wormholes” pode mudar a estrutura da rede significamente, dependendo da taxa a que são introduzidos. Se introduzirmos ”wormholes” a uma taxa constante, o diâmetro da rede apresenta um crescimento sub-logarítmico com o número de nodos do sistema.
We study complex networks formed by triangulations of evolving manifolds, locally homeomorphic to a plane. The set of possible transformations of these networks is restricted by the condition that at each step all the faces must be triangles. We employed two main approaches. In the first approach we grow the manifolds using various simples rules, which progressively had new triangles. In the other approach we relax the structure of large manifolds while keeping the number of triangles constant. The networks resulting from these evolving triangulations demonstrate several interesting features, unexpected in planar networks, such as small-world diameters and power-law degree distributions. Finally, we manipulate the topology of the manifolds by introducing wormholes. The presence of wormholes can change significantly the network structure, depending on the rate at which they are introduced. Remarkably, if we make wormholes at constant rate, the network’s diameter shows a sub-logarithmic growth with the number of nodes in the system.
9
Bertagnolli, Giulia. "Modelling the process-driven geometry of complex networks." Doctoral thesis, Università degli studi di Trento, 2022. http://hdl.handle.net/11572/346519.
Full textAbstract:
Graphs are a great tool for representing complex physical and social systems, where the interactions among many units, from tens of animal species in a food-web, to millions of users in a social network, give rise to emergent, complex system behaviours. In the field of network science this representation, which is usually called a complex network, can be complicated at will to better represent the real system under study. For instance, interactions may be directed or may differ in their strength or cost, leading to directed weighted networks, but they may also depend on time, like in temporal networks, or nodes (i.e. the units of the system) may interact in different ways, in which case edge-coloured multi-graphs and multi-layer networks represent better the system. Besides this rich repertoire of network structures, we cannot forgot that edges represent interactions and that this interactions are not static, but are, instead, purposely established to reach some function of the system, as for instance, routing people and goods through a transportation network or cognition, through the exchange of neuro-physiological signals in the brain. Building on the foundations of spectral graph theory, of non-linear dimensionality reduction and diffusion maps, and of the recently introduced diffusion distance [Phys. Rev. Lett. 118, 168301 (2017)] we use the simple yet powerful tool of continuous-time Markov chains on networks to model their process-driven geometry and characterise their functional shape. The main results are: (i) the generalisation of the diffusion geometry framework to different types of interconnected systems (from edge-coloured multigraphs to multi-layer networks) and of random walk dynamics [Phys. Rev. E 103, 042301 (2021)] and (ii) the introduction of new descriptors based on the diffusion geometry to quantify and describe the micro- (through the network depth [J. Complex Netw. 8, 4 (2020)]), meso- (functional rich-club) and macro-scale (using statistics of the pairwise distances between the network's nodes [Comm. Phys. 4, 125 (2021)]) of complex networks.
10
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.
Full text
11
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.
Full text
12
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/.
Full textAbstract:
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.
13
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.
Full text
14
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.
Full textAbstract:
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.
15
Bockholt, Mareike [Verfasser], and 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.
Full text
16
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.
Full textAbstract:
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.
17
Zhou, Shu. "Exploring network models under sampling." Kansas State University, 2015. http://hdl.handle.net/2097/20349.
Full textAbstract:
Master of ScienceDepartment of Statistics
Perla Reyes
Networks are defined as sets of items and their connections. Interconnected items are represented by mathematical abstractions called vertices (or nodes), and the links connecting pairs of vertices are known as edges. Networks are easily seen in everyday life: a network of friends, the Internet, metabolic or citation networks. The increase of available data and the need to analyze network have resulted in the proliferation of models for networks. However, for networks with billions of nodes and edges, computation and inference might not be achieved within a reasonable amount of time or budget. A sampling approach seems a natural choice, but traditional models assume that we can have access to the entire network. Moreover, when data is only available for a sampled sub-network conclusions tend to be extrapolated to the whole network/population without regard to sampling error. The statistical problem this report addresses is the issue of how to sample a sub-network and then draw conclusions about the whole network. Are some sampling techniques better than others? Are there more efficient ways to estimate parameters of interest? In which way can we measure how effectively my method is reproducing the original network? We explore these questions with a simulation study on Mesa High School students' friendship network. First, to assess the characteristics of the whole network, we applied the traditional exponential random graph model (ERGM) and a stochastic blockmodel to the complete population of 205 students. Then, we drew simple random and stratified samples of 41 students, applied the traditional ERGM and the stochastic blockmodel again, and defined a way to generalized the sample findings to the population friendship network of 205 students. Finally, we used the degree distribution and other network statistics to compare the true friendship network with the projected one. We achieved the following important results: 1) as expected stratified sampling outperforms simple random sampling when selecting nodes; 2) ERGM without restrictions offers a poor estimate for most of the tested parameters; and 3) the Bayesian stochastic blockmodel estimation using a strati ed sample of nodes achieves the best results.
18
Leung, Chi-chung. "Modelling complex network dynamics a statistical physics approach /." Click to view the E-thesis via HKUTO, 2006. http://sunzi.lib.hku.hk/hkuto/record/B38324611.
Full text
19
Spencer, Matthew. "Evolving complex network models of functional connectivity dynamics." Thesis, University of Reading, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.590143.
Full textAbstract:
Functional connectivity networks describe how regions of the brain interact. The timing, location, and frequency of these interactions inform about memory, decision making, motor movement, affective states, and more. However, while these interactions are well described as networks, these networks, like many others throughout nature, are constantly changing. Complex network evolution poses a highly dimensional problem but also contains much information about the system in question. In this thesis, a recent class of evolving complex network models was explored and extended to capture the functional connectivity dynamics observed in neuronal networks. Functional connectivity was investigated through data- and model-driven techniques at the cellular level, with cultures of cortical neurones on multi-electrode arrays, and at the whole-brain level, with electroencephalography. At the neuronal level, complex spatial dependencies were identified in bursts of excitation and two novel network models, the Starburst model and the Excitation Flow model, are used to capture the resulting functional connectivity. At the whole-brain level, functional connectivity dynamics were used to perform single-trial classification of intentional motor movement. Again, spatiotemporal dependencies were identified and used to present three novel techniques for modelling the network dynamics. The first two techniques decomposed networks into network templates (one model-based and one spectral-based) and modelled the dynamics with hidden Markov models. The final technique was a generalised evolving version of the Starburst model. The hidden Markov model of spectrally decomposed networks was shown to classify motor intentions with an accuracy around 80%. Firstly, this thesis shows that time plays an important role in the production of the complex network topologies observed in functional connectivity, both at the cellular and whole-brain leve1. Further, it is shown that evolving complex network models are very useful tools for modelling these topologies and that the network dynamics can be used to uncover features that are crucial to identifying functional states.
20
Leung, Chi-chung, and 梁志聰. "Modelling complex network dynamics: a statistical physics approach." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2006. http://hub.hku.hk/bib/B38324611.
Full text
21
Herrmann, Sebastian [Verfasser]. "Complex network analysis of fitness landscapes / Sebastian Herrmann." Mainz : Universitätsbibliothek Mainz, 2017. http://d-nb.info/1122760159/34.
Full text
22
Kitromilidis, Michail Emmanouil. "Topics of interdisciplinary applications of complex network theory." Thesis, Imperial College London, 2018. http://hdl.handle.net/10044/1/62640.
Full textAbstract:
In this thesis we explore the interdisciplinary applications of complex network theory, by looking at how it has facilitated the analysis of numerous academic fields and by proposing new ways for the analysis of container shipping networks, artistic influence and economics and financial systems. We consistently use the same concepts across the various applications, built around centrality and community detection, but we adapt them depending on the application in order to illustrate how similar tools can provide a significant insight into the system in question.
23
Hui, Zi. "Spatial structure of complex network and diffusion dynamics." Thesis, Le Mans, 2013. http://www.theses.fr/2013LEMA1005/document.
Full textAbstract:
Dans le développement récent des sciences de réseau, réseaux contraints spatiales sont devenues un objet d'une enquête approfondie. Spatiales des réseaux de contraintes sont intégrées dans l'espace de configuration. Leurs structures et les dynamiques sont influencées par la distance spatiale. Ceci est prouvé par les données empiriques de plus en plus sur des systèmes réels montrant des lois exponentielles ou de distribution d'énergie distance spatiale de liens. Dans cette thèse, nous nous concentrons sur la structure de réseau spatial avec une distribution en loi de puissance spatiale. Plusieurs mécanismes de formation de la structure et de la dynamique de diffusion sur ces réseaux sont pris en considération. D'abord, nous proposons un réseau évolutif construit en l'espace de configuration d'un mécanisme de concurrence entre le degré et les préférences de distance spatiale. Ce mécanisme est décrit par un a^'fc- + (1 — a)^'lL_,1, où ki est le degré du noeud i et rni est la distance spatiale entre les noeuds n et i. En réglant le paramètre a, le réseau peut être fait pour changer en continu à partir du réseau spatiale entraînée (a = 0) pour le réseau sans échelle (a = 1). La structure topologique de notre modèle est comparé aux données empiriques de réseau de courrier électronique avec un bon accord. Sur cette base, nous nous concentrons sur la dynamique de diffusion sur le réseau axé sur spatiale (a — 0). Le premier modèle, nous avons utilisé est fréquemment employée dans l'étude de la propagation de l'épidémie: ['spatiale susceptible-infecté-susceptible (SIS) modèle. Ici, le taux de propagation entre deux noeuds connectés est inversement proportionnelle à leur distance spatiale. Le résultat montre que la diffusion efficace de temps augmente avec l'augmentation de a. L'existence d'seuil épidémique générique est observée, dont la valeur dépend du paramètre a Le seuil épidémique maximum et le ratio minimum fixe de noeuds infectés localiser simultanément dans le intervalle 1.5 < a < 2.Puisque le réseau spatiale axée a bien défini la distance spatiale, ce modèle offre une occasion d'étudier la dynamique de diffusion en utilisant les techniques habituelles de la mécanique statistique. Tout d'abord, compte tenu du fait que la diffusion est anormale en général en raison de l'importante long plage de propagation, nous introduisons un coefficient de diffusion composite qui est la somme de la diffusion d'habitude constante D des lois de la Fick appliqué sur différentes distances de transfert possibles sur le réseau. Comme prévu, ce coefficient composite diminue avec l'augmentation de a. et est une bonne mesure de l'efficacité de la diffusion. Notre seconde approche pour cette diffusion anormale est de calculer le déplacement quadratique moyen (l²) à identifier une constante de diffusion D' et le degré de la anomalousness y avec l'aide de la loi de puissance (l²) = 4D'ty. D' comportements de la même manière que D, i.e.. elle diminue avec l'augmentation de a. y est inférieur à l'unité (subdiffusion) et tend à un (diffusion normale) que a augmenteIn the recent development of network sciences, spatial constrained networks have become an object of extensive investigation. Spatial constrained networks are embedded in configuration space. Their structures and dynamics are influenced by spatial distance. This is proved by more and more empirical data on real Systems showing exponential or power laws spatial distance distribution of links. In this dissertation, we focus on the structure of spatial network with power law spatial distribution. Several mechanisms of structure formation and diffusion dynamics on these networks are considered. First we propose an evolutionary network constructed in the configuration space with a competing mechanism between the degree and the spatial distance preferences. This mechanism is described by a ki + (1 — a), where ki is the degree of node i and rni is the spatial distance between nodes n and i. By adjusting parameter a, the network can be made to change continuously from the spatial driven network (a = 0) to the scale-free network (a = 1). The topological structure of our model is compared to the empirical data from email network with good agreement. On this basis, we focus on the diffusion dynamics on spatial driven network (a = 0). The first model we used is frequently employed in the study of epidemie spreading : the spatial susceptible-infected-susceptible (SIS) model. Here the spreading rate between two connected nodes is inversely proportional to their spatial distance. The result shows that the effective spreading time increases with increasing a. The existence of generic epidemic threshold is observed, whose value dépends on parameter a. The maximum épidemic threshold and the minimum stationary ratio of infected nodes simultaneously locate in the interval 1.5 < a < 2. Since the spatial driven network has well defined spatial distance, this model offers an occasion to study the diffusion dynamics by using the usual techniques of statistical mechanics. First, considering the fact that the diffusion is anomalous in general due to the important long-range spreading, we introduce a composite diffusion coefficient which is the sum of the usual diffusion constant D of the Fick's laws applied over different possible transfer distances on the network. As expected, this composite coefficient decreases with increasing a and is a good measure of the efficiency of the diffusion. Our second approach to this anomalous diffusion is to calculate the mean square displacement (l²) to identify a diffusion constant D' and the degree of thé anomalousness y with the help of the power law {l²} = 4D'ty. D' behaviors in the same way as D, i.e., it decreases with increasing a. y is smaller than unity (subdiffusion) and tends to one (normal diffusion) as a increases
24
Garg, Arun. "Quantifying resilient safety culture using complex network theory." Thesis, Griffith University, 2022. http://hdl.handle.net/10072/411532.
Full textAbstract:
Safety is defined as the absence of accidents where accident is an event which lead to unacceptable loss. Previously, most systems employed conventional risk management systems to deal with risks which was based on knowledge of previous experiences, failure reporting and risk assessments by computing historic data. But today, these are traced to organizational factors, functional performance variability and unexpected outcomes or it can be pointed towards systems thinking. Resilience engineering is recognized as other alternative to traditional approaches in safety management. The idea behind resilience engineering is that an organization must continually manage risks and create an anticipating, monitoring, responding and learning culture. This is resilient safety culture. Resilient safety culture is a new concept which has been proposed in order to cover the weaknesses of traditional approaches of safety culture. It is a safety culture with resilience, learning, continuous improvements and cost effectiveness. This resilience takes into consideration the dynamic aspect of the safety culture which makes it resilient to any risks which a safety system faces. The main drawback is the dynamic aspect of the culture is not taken into consideration which is the interaction between people, technology and administration. These interactions are quite complex in nature and difficult to understand and quantify. That is why this study investigates the understanding of these interactions using complex network theory. Once these interactions are understood to some extent, the prediction and prevention of incidents can be done to some extent.
There are four different kinds of indicators in the system. Two are system performance indicators, leading and lagging and the other two are the risk indicators that as well leading and lagging. The system performance indicators are indicators which show how the system is performing either in current state which is leading and the system performance indicator which is lagging is gauged by efficiency of the system after a time such as injury rate. Risk indicators leading is found by understanding the various risks which are prevalent in the system and lagging risk indicators are the indicators which led to an accident in previous time frame. Since the system is dynamic, it needs to be understood that these indicators have a time value attached to it. If there is an accident which happened due to some lagging risk indicator, that is in previous time frame, that may have already changed by the time the accidents happened so safety-1 concept which looked at just lagging indicators to dictate the future evaluations of the organization need to be modified and thus resilient safety culture methodology is getting evolved using resilience engineering.
Using fault tree analysis, the interactions of various components in a safety system can be understood. Resilient safety culture is treated as a system and it has three sub systems. The sub system further has factors which are important relationships to understand the whole system. These relations between the factors and subsystem are used to measure the resilience of the whole system. This is an innovative quantifying way in which we can improve the resilience in safety culture of an organization.
In this study, the qualitative variables defined using the literature are correlated using qualitative as well as quantitative approaches. In the qualitative approach, Leximancer tool is used which model the variables using the literature data. Next, the resilient safety culture model is generated and then fault tree analysis is used to decipher the complex interactions which can help understand which relationships can lead to incident. This study would generate a tool which would help organizations look at the weak links and nodes in their organization to better equip and enhance resources to make the organization more resilient against any safety risks. Multiple case studies are done to validate this model and to show how the whole process is done to understand a way to reduce and mitigate risks. Resilience index is generated which helps in finding which constructs are lagging or weak in giving that index number and the index can be used to compare to companies or organizations irrespective of the number of respondents or the type of indicators which are used. It also helps in reducing the linguistic bias.
The findings of this study show that in resilient safety culture model, which components should be focussed first and how the components of resilient safety culture model are related with each other. This helps in optimization of the components or subcomponents to get the maximum resilience in an organization. It is also found that weak areas in an organization can be successfully deciphered using the fault tree analysis approach along with visualization of failure paths.
This resilience safety culture model generated along with the methodology adopted in this study can help the industry to making right decisions in enhancing the resilience of the organizations with minimum intervention. It can help the industry find the weak areas where the intervention is needed. It can also give leading indicators which can cause future incidents.Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Eng & Built Env
Science, Environment, Engineering and Technology
Full Text
25
Cheng, Chih Kang. "Hardware implementation of the complex Hopfield neural network." CSUSB ScholarWorks, 1995. https://scholarworks.lib.csusb.edu/etd-project/1016.
Full text
26
Battini, Daria. "Dynamic modeling of networks and logistic complex systems." Doctoral thesis, Università degli studi di Padova, 2008. http://hdl.handle.net/11577/3426267.
Full textAbstract:
Modern supply chains usually provide very complex inter- correlations between various actors: suppliers, manufacturers, distributors, customers, etc. Such inter-correlations are not only based on material flows but also on data and financial flows.
Discussions about alternatives for traditional goods and services distribution in the company are becoming more frequent, as the constantly increasing demands and requirements of the market put pressure on suppliers and manufacturers logistics. Therefore, this need is emphasized by the growing of industrial systems complexity and its indirect and drown costs, increasing day by day.
The terms Supply Network and Business Web are now interchangeable in the way they are used to summarise flow in supply chains (Tapscott, 2000).
Distribution Webs and Supply Networks are urgently demanding new effective management strategies to preserve competitiveness, increase organization and control the complexity level increment.
This dissertation touches upon the fundamental theories of Distribution Network Optimization and Supply Network Complexity Analysis, it proposes new techniques to characterize peculiar Supply Network aspects and underline the importance of adequate systemic approaches and software support in the development of this particular discipline.
This work has four main goals:
1. Show how Goods Delivery Distribution Optimization is feasible and critical to creates efficient networks
2. Investigate how the issue of Distribution Network Design is crucial in order to increase efficiency and competitiveness
3. Assess the performance of new algorithms for industrial network complexity control and computation;
4. Develop new quantitative measurements of complexity for supply networks based on Network Analysis, which is often used to study natural ecosystems, focusing in particular on the concept of entropy of information (derived by Shannon, 1948).
All these accomplishment are associated with appropriate software applications.
The dissertation is divided in three Parts (1. Theoretical framework, 2. New network analysis methodologies development, 3. Three published papers collection).
This work, conducted with a profitable interdisciplinary collaboration with the Department of Ecology and Evolutionary Biology at Michigan University (Ann Arbour), is devoted to investigate alternatives for goods distribution in Supply Networks and develop advances in both theories on Supply Network Design problem and on its application to industrial contexts.
The new interdisciplinary approaches developed exploit new performances indexes to map the exchange of goods and information between different actors in a complex supply chain and show how Network Analysis and systemic approaches are relevant tools in providing a new perspective in defining supply network organization and complexity.
27
Garuccio, Elena. "Reconstruction, modelling and analysis of economic networks." Doctoral thesis, Università di Siena, 2018. http://hdl.handle.net/11365/1059854.
Full textAbstract:
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.
28
Novelli, Leonardo. "Relating network structure and function via information theory." Thesis, University of Sydney, 2020. https://hdl.handle.net/2123/24093.
Full textAbstract:
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.
29
Weighill, Deborah A. "Exploring the topology of complex phylogenomic and transcriptomic networks." Thesis, Stellenbosch : Stellenbosch University, 2014. http://hdl.handle.net/10019.1/95800.
Full textAbstract:
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.
30
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.
Full textAbstract:
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.
31
Trusina, Ala. "Complex Networks : Structure, Function , Evolution." Doctoral thesis, Umeå University, Physics, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-608.
Full textAbstract:
A complex system is a system for which the statement "the whole is greater than the sum of its parts" holds. A network can be viewed as a backbone of a complex system. Combining the knowledge about the entities constituting the complex system with the properties of the interaction patterns we can get a better understanding of why the whole is greater than the sum. One of the purposes of network studies, is to relate the particular structural and dynamical properties of the network to the function it is designed to perform. In the present work I am briefly presenting some of the advances that have been achieved in the field of the complex networks together with the contributions which I have been involved in.
32
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/.
Full textAbstract:
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 importantesComplex 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
33
Hannesson, Kristófer. "Identifying Important Members in a Complex Online Social Network." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-216947.
Full textAbstract:
The success of Online Social Networks (OSN) is influenced by having the ability to understand who is important. An OSN can be viewed as a graph where users are vertices and their interactions are edges. Graph-based methods can enable identification of people in these networks who for example exhibit the characteristics of leaders, influencers, or information brokers. A Massively Multiplayer Online game (MMO) is a type of OSN. It is a video game where a large number of players interact with each other in a virtual world. Using behavioral data of players' interactions within the space-based MMO EVE Online, the aim of this thesis is to conduct an experimental study to evaluate the effectiveness of a number graph-based methods at finding important players within different behavioral contexts. For that purpose we extract behavioral data to construct four distinct graphs: Fleet, Aggression, Mail, and Market. We also create a ground truth data set of important players based on heuristics from key gameplay categories. We experiment on these graphs with a selection of graph centrality, Influence Maximization, and heuristic methods. We explore how they perform in terms of ground truth players found per graph and execution time, and when combining results from all graphs. Our results indicate that there is no optimal method across graphs but rather the method and graph should be chosen according to the business intention at each time. To that end we provide recommendations as well as potential business case usages. We believe that this study serves as a starting point towards more graph based analysis within the EVE Online virtual universe where there are many unexplored research opportunities.Framgången hos Online Sociala Nätverk (OSN) påverkas av förmågan att förstå vem som är viktig. Ett OSN kan ses som en graf där användarna är noder och deras interaktioner ärbågar. Grafbaserade metoder kan möjliggöra identifiering av personer i dessa nätverk somtill exempel uppvisar egenskaper hos ledare, påverkare eller informationsförmedlare. Ett Massively Multiplayer Online game (MMO) representerar en typ av OSN. Det är ett datorrollspel där ett stort antal spelare interagerar med varandra i en virtuell värld. Genomatt använda beteendedata om spelarnas interaktioner i den rymdbaserade MMO:n EVE Online är målet med denna avhandling att genomföra en experimentell studie för att utvärdera effektiviteten hos ett antal grafbaserade metoder för att hitta viktiga spelare inom olika beteendemässiga sammanhang. För det ändamålet extraherar vi beteendedata för att konstruera fyra distinkta grafer: Fleet, Aggression, Mail och Market. Vi skapar också ett ground truth" dataset av viktiga spelare baserat på heuristik från viktiga spelkategorier. Vi utför experiment på dessa grafer med ett urval av grafcentralitet, Influence Maximization och heuristiska metoder. Vi undersöker hur metoderna presterar i termer av antal ground truth spelare som finns per graf och över grafer, och i termer av exekveringstid. Våra resultat tyder på att det inte finns någon optimal metod för alla grafer. Metoden och grafen bör väljas beroende på intentionen vid varje tillfälle. För detta ändamål tillhandahåller vi rekommendationer samt potentiella affärsmässiga användningsområden. Vi tror att denna studie tjänar som utgångspunkt för mer grafbaserad analys inom EVEOnlines virtuella universum där det finns många outforskade forskningsmöjligheter.
34
Choe, Sehyo Charley. "Models of complex adaptive systems with underlying network structure." Thesis, University of Oxford, 2007. https://ora.ox.ac.uk/objects/uuid:1cb8cb96-d27f-4543-9065-0e38a4297435.
Full textAbstract:
This thesis explores the effect of different types of underlying network structure on the dynamical behaviour of a competitive population - a situation encountered in many real-world complex systems. In the first part of the thesis, I focus on generic, but abstract, multi-agent systems. I start by presenting analytic and numerical results for a population of heterogeneous, decision-making agents competing for some limited global resource, in which connections arise unintentionally between agents as a by-product of their strategy choices. I show that accounting for the resulting groups of strongly-correlated agents - in particular, the crowds and so-called 'anticrowds' - yields an accurate analytic description of the systems dynamics. I then introduce a local communication network between the agents, enabling them to intentionally share information among themselves. Such an interaction network leads to highly non-trivial dynamics, forcing a trade-off between individual and global success. Introducing corruption into the information being exchanged between agents, gives rise to a novel phase transition. I then provide a quantitative analytic theory of these various numerical results by generalizing the Crowd-Anticrowd formalism to include such local interactions. In the second part of the thesis, I consider a real-world system which also features competitive populations and networks - a cancer tumour, which contains cancerous cells competing for space and nutrients in the presence of an underlying vasculature structure. To simplify the analysis and comparison to real clinical data, the model chosen is far simpler than that discussed in the first part of the thesis - however despite its simplicity, the model is shown to yield remarkably good agreement with empirical findings. In addition, the model shows how different treatment methods can lead to a wide variety of unexpected re-growth behaviours of the tumour.
35
Yu, Joseph Hon. "Auto-configuration of Savants in a complex, variable network." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/33378.
Full textAbstract:
Thesis (M. Eng. and S.B.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.Includes bibliographical references (p. 63-64).
In this thesis, present a system design that enables Savants to automatically configure both their network settings and their required application programs when connected to an intelligent data management and application system. Savants are intelligent routers in a large network used to manage the data and events related to communications with electronic identification tags [10]. The ubiquitous nature of the identification tags and the access points that communicate with them requires an information and management system that is equally ubiquitous and able to deal with huge volumes of data. The Savant systems were designed to be such a ubiquitous information and management system. Deploying any ubiquitous system is difficult, and automation is required to streamline its deployment and improve system management, reliability, and performance. My solution to this auto-configuration problem uses NETCONF as a standard language and protocol for configuration communication among Savants. It also uses the Content-Addressable Network (CAN) as a discovery service to help Savants locate configuration information, since a new Savant may not have information about the network structure. With these tools, new Savants can configure themselves automatically with the help of other Savants.
(cont.) Specifically, they can configure their network settings, download and set up software, and integrate with network distributed applications. Future work could expand upon my project by studying an implementation, making provisions for resource-limited Savants, or improving security.
by Joseph Hon Yu.
M.Eng.and S.B.
36
Urio, Paulo Roberto. "Complex network component unfolding using a particle competition technique." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-14092017-091318/.
Full textAbstract:
This work applies complex network theory to the problem of semi-supervised and unsupervised learning in networks that are representations of multivariate datasets. Complex networks allow the use of nonlinear dynamical systems to represent behaviors according to the connectivity patterns of networks. Inspired by behavior observed in nature, such as competition for limited resources, dynamical system models can be employed to uncover the organizational structure of a network. In this dissertation, we develop a technique for classifying data represented as interaction networks. As part of the technique, we model a dynamical system inspired by the biological dynamics of resource competition. So far, similar methods have focused on vertices as the resource of competition. We introduce edges as the resource of competition. In doing so, the connectivity pattern of a network might be used not only in the dynamical system simulation but in the learning task as well.Este trabalho aplica a teoria de redes complexas para o estudo de uma técnica aplicada ao problema de aprendizado semissupervisionado e não-supervisionado em redes, especificamente, aquelas que representam conjuntos de dados multivariados. Redes complexas permitem o emprego de sistemas dinâmicos não-lineares que podem apresentar comportamentos de acordo com os padrões de conectividade de redes. Inspirado pelos comportamentos observados na natureza, tais como a competição por recursos limitados, sistema dinâmicos podem ser utilizados para revelar a estrutura da organização de uma rede. Nesta dissertação, desenvolve-se uma técnica aplicada ao problema de classificação de dados representados por redes de interação. Como parte da técnica, um sistema dinâmico inspirado na competição por recursos foi modelado. Métodos similares concentraram-se em vértices como o recurso da concorrência. Neste trabalho, introduziu-se arestas como o recurso-alvo da competição. Ao fazê-lo, utilizar-se-á o padrão de conectividade de uma rede tanto na simulação do sistema dinâmico, quanto na tarefa de aprendizado.
37
Banerji, C. "Network theoretic tools in the analysis of complex diseases." Thesis, University College London (University of London), 2015. http://discovery.ucl.ac.uk/1470036/.
Full textAbstract:
In this thesis we consider the application of network theoretic tools in the analysis of genome wide gene-expression data describing complex diseases, displaying defects in differentiation. After considering the literature, we motivate the construction of entropy based network rewiring methodologies, postulating that such an approach may provide a systems level correlate of the differentiation potential of a cellular sample, and may prove informative in the analysis of pathology. We construct, analytically investigate and validate three such network theoretic tools: Network Transfer Entropy, Signalling Entropy and Interactome Sparsification and Rewiring (InSpiRe). By considering over 1000 genome wide gene expression samples corresponding to healthy cells at different levels of differentiation, we demonstrate that signalling entropy is a strong correlate of cell potency confirming our initial postulate. The remainder of the thesis applies our network theoretic tools to two ends of the developmental pathology spectrum. Firstly we consider cancer, in which the power of cell differentiation is hijacked, to develop a malicious new tissue. Secondly, we consider muscular dystrophy, in which cell differentiation is inhibited, resulting in the poor development of muscle tissue. In the case of cancer we demonstrate that signalling entropy is a measure of tumour anaplasia and intra-tumour heterogeneity, which displays distinct values in different cancer subtypes. Moreover, we find signalling entropy to be a powerful prognostic indicator in epithelial cancer, outperforming conventional gene expression based assays. In the case of muscular dystrophy we focus on the most prevalent: facioscapulohumeral muscular dystrophy (FSHD). We demonstrate that muscle differentiation is perturbed in FSHD and that signalling entropy is elevated in myoblasts over-expressing the primary FSHD candidate gene DUX4. We subsequently utilise InSpiRe, performing a meta-analysis of FSHD muscle biopsy gene-expression data, uncovering a network of DUX4 driven rewired interactions in the pathology, and a novel therapeutic target which we validate experimentally.
38
Shakeri, Heman. "Complex network analysis using modulus of families of walks." Diss., Kansas State University, 2017. http://hdl.handle.net/2097/35525.
Full textAbstract:
Doctor of PhilosophyDepartment of Electrical and Computer Engineering
Pietro Poggi-Corradini
Caterina M. Scoglio
The modulus of a family of walks quanti es the richness of the family by favoring having many short walks over a few longer ones. In this dissertation, we investigate various families of walks to study new measures for quantifying network properties using modulus. The proposed new measures are compared to other known quantities. Our proposed method is based on walks on a network, and therefore will work in great generality. For instance, the networks we consider can be directed, multi-edged, weighted, and even contain disconnected parts. We study the popular centrality measure known in some circles as information centrality, also known as e ective conductance centrality. After reinterpreting this measure in terms of modulus of families of walks, we introduce a modi cation called shell modulus centrality, that relies on the egocentric structure of the graph. Ego networks are networks formed around egos with a speci c order of neighborhoods. We then propose e cient analytical and approximate methods for computing these measures on both directed and undirected networks. Finally, we describe a simple method inspired by shell modulus centrality, called general degree, which improves simple degree centrality and could prove to be a useful tool for practitioners in the applied sciences. General degree is useful for detecting the best set of nodes for immunization. We also study the structure of loops in networks using the notion of modulus of loop families. We introduce a new measure of network clustering by quantifying the richness of families of (simple) loops. Modulus tries to minimize the expected overlap among loops by spreading the expected link-usage optimally. We propose weighting networks using these expected link-usages to improve classical community detection algorithms. We show that the proposed method enhances the performance of certain algorithms, such as spectral partitioning and modularity maximization heuristics, on standard benchmarks. Computing loop modulus bene ts from e cient algorithms for nding shortest loops, thus we propose a deterministic combinatorial algorithm that nds a shortest cycle in graphs. The proposed algorithm reduces the worst case time complexity of the existing combinatorial algorithms to O(nm) or O(hkin2 log n) while visiting at most m - n + 1 cycles (size of cycle basis). For most empirical networks with average degree in O(n1 ) our algorithm is subcubic.
39
Boers, Niklas. "Complex network analysis of extreme rainfall in South America." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät, 2015. http://dx.doi.org/10.18452/17237.
Full textAbstract:
Basierend auf der Theorie von Netzwerken wird ein allgemeines Rahmenwerk entwickelt, um kollektive Synchronisationsphänome von Extremereignissen in komplexen Systemen zu studieren. Die Methode vergleicht die Variabilität der einzelnen Teile des Systems auf Grundlage von Beobachtungszeitreihen mit dem Ziel, emergente Synchronisationsmuster von Extremereignissen auf makroskopischer Ebene aufzudecken. Zu diesem Zweck werden die einzelnen Zeitreihen eines interaktiven Systems mit den Knoten eines Netzwerks identifiziert und die Abhängigkeiten zwischen diesen durch die Kanten des Netzwerks dargestellt. Die komplexe interne Synchronisationsstruktur des Systems wird so in Form der Netzwerktopologie mathematisch zugänglich gemacht und kann durch die Einführung geeigneter Netzwerkmaße analysiert werden. Die Methode wird im Folgenden auf räumlich und zeitlich hochaufgelöste Regendaten aus Satellitenmessungen angewendet, um die kollektive Dynamik extremer Regenereignisse in Südamerika zu untersuchen. Diese Anwendung verfolgt drei Ziele: Erstens wird gezeigt, wie die hier entwickelte Methode zur klimatologischen Analyse verwendet werden kann. Zweitens können Quellen und Senken von Extremereignissen durch die Einführung des Konzeptes der Netzwerkdivergenz identifiziert werden. Dies erlaubt es, die gerichteten Netzwerkpfade, entlang derer Extremereignisse synchronisieren, nachzuverfolgen. Auf dieser Grundlage wird eine statistische Regel gewonnen, die beträchtliche Anteile der extremen Regenereignisse in den Zentralanden vorhersagt. Drittens werden die bis dahin entwickelten Methoden und gewonnenen Einsichten dazu verwendet, die Darstellung extre- mer Regenereignisse in verschiedenen Datensätzen zu vergleichen. Insbesondere wird in diesem Kontext die Implementierung solcher Ereignisse in drei gängigen Klimamodellen evaluiert.Based on the theory of networks, a general framework is developed to study collective synchronization phenomena of extreme events in complex systems. The method relies on observational time series encoding the variability of the single parts of the system, and is intended to reveal emerging patterns of extreme event synchronization on the macroscopic level. For this purpose, the time series obtained from an interactive system under consideration are identified with network nodes, and the possibly delayed and non-linear interdependence of extreme events in different time series is represented by network links connecting the nodes. In this way, the complex internal synchronization structure of the system becomes accessible in terms of the topology of the network, which can be analyzed by introducing suitable network measures. The methodology is applied to satellite-derived rainfall time series of high spatiotemporal resolution in order to investigate the collective dynamics of extreme rainfall events in South America. The purpose of this application is threefold: First, it is shown how the methodology can be used for climatic analysis by revealing climatological mechanism from the spatial patterns exhibited by different network measures. Second, by introducing the concept of network divergence, sink and source regions of extreme events can be identified, allowing to track their directed synchronization pathways through the network. A simple statistical forecast rule is derived on this basis, predicting substantial fractions of extreme rainfall events in the Central Andes. Third, the methodology and the insights developed in the first two steps are used to evaluate the dynamical representation of extreme events in different datasets, and in particular their dynamical implementation in three state of the art climate models.
40
Schmeltzer, Christian. "Dynamical properties of neuronal systems with complex network structure." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät, 2016. http://dx.doi.org/10.18452/17470.
Full textAbstract:
In welcher Weise hängt die Dynamik eines neuronalen Systems von den Eigenschaften seiner Netzwerkstruktur ab? Diese wichtige Fragestellung der Neurowissenschaft untersuchen wir in dieser Dissertation anhand einer analytischen und numerischen Modellierung der Aktivität großer neuronaler Netzwerke mit komplexer Struktur. Im Fokus steht die Relevanz zweier bestimmter Merkmale für die Dynamik: strukturelle Heterogenität und Gradkorrelationen. Ein zentraler Bestandteil der Dissertation ist die Entwicklung einer Molekularfeldnäherung, mit der die mittlere Aktivität heterogener, gradkorrelierter neuronaler Netzwerke berechnet werden kann, ohne dass einzelne Neuronen explizit simuliert werden müssen. Die Netzwerkstruktur wird von einer reduzierten Matrix erfasst, welche die Verbindungsstärke zwischen den Neuronengruppen beschreibt. Für einige generische Zufallsnetzwerke kann diese Matrix analytisch berechnet werden, was eine effiziente Analyse der Dynamik dieser Systeme erlaubt. Mit der Molekularfeldnäherung und numerischen Simulationen zeigen wir, dass assortative Gradkorrelationen einem neuronalen System ermöglichen, seine Aktivität bei geringer externer Anregung aufrecht zu erhalten und somit besonders sensitiv auf schwache Stimuli zu reagieren.An important question in neuroscience is how the structure and dynamics of a neuronal network relate to each other. We approach this problem by modeling the spiking activity of large-scale neuronal networks that exhibit several complex network properties. Our main focus lies on the relevance of two particular attributes for the dynamics, namely structural heterogeneity and degree correlations. As a central result, we introduce a novel mean-field method that makes it possible to calculate the average activity of heterogeneous, degree-correlated neuronal networks without having to simulate each neuron explicitly. We find that the connectivity structure is sufficiently captured by a reduced matrix that contains only the coupling between the populations. With the mean-field method and numerical simulations we demonstrate that assortative degree correlations enhance the network’s ability to sustain activity for low external excitation, thus making it more sensitive to small input signals.
41
Phang, Chang. "Differential equation and complex network approaches for epidemic modelling." Thesis, Curtin University, 2012. http://hdl.handle.net/20.500.11937/1343.
Full textAbstract:
This study consists of three parts. The first part focuses on bifurcation analysis of epidemic models with sub-optimal immunity and saturated treatment/recovery rate as well as nonlinear incidence rate. The second part of the research focuses on estimating the domain of attraction for sub-optimal immunity epidemic models. In the third part of the research, we develop a bond percolation model for community clustered networks with an arbitrarily specified joint degree distribution.
42
Johnson, Sandra. "Integrated Bayesian network frameworks for modelling complex ecological issues." Thesis, Queensland University of Technology, 2009. https://eprints.qut.edu.au/32002/1/Sandra_Johnson_Thesis.pdf.
Full textAbstract:
Ecological problems are typically multi faceted and need to be addressed from a scientific and a management perspective. There is a wealth of modelling and simulation software available, each designed to address a particular aspect of the issue of concern. Choosing the appropriate tool, making sense of the disparate outputs, and taking decisions when little or no empirical data is available, are everyday challenges facing the ecologist and environmental manager.
Bayesian Networks provide a statistical modelling framework that enables analysis and integration of information in its own right as well as integration of a variety of models addressing different aspects of a common overall problem. There has been increased interest in the use of BNs to model environmental systems and issues of concern. However, the development of more sophisticated BNs, utilising dynamic and object oriented (OO) features, is still at the frontier of ecological research. Such features are particularly appealing in an ecological context, since the underlying facts are often spatial and temporal in nature.
This thesis focuses on an integrated BN approach which facilitates OO modelling. Our research devises a new heuristic method, the Iterative Bayesian Network Development Cycle (IBNDC), for the development of BN models within a multi-field and multi-expert context. Expert elicitation is a popular method used to quantify BNs when data is sparse, but expert knowledge is abundant. The resulting BNs need to be substantiated and validated taking this uncertainty into account. Our research demonstrates the application of the IBNDC approach to support these aspects of BN modelling.
The complex nature of environmental issues makes them ideal case studies for the proposed integrated approach to modelling. Moreover, they lend themselves to a series of integrated sub-networks describing different scientific components, combining scientific and management perspectives, or pooling similar contributions developed in different locations by different research groups.
In southern Africa the two largest free-ranging cheetah (Acinonyx jubatus) populations are in Namibia and Botswana, where the majority of cheetahs are located outside protected areas. Consequently, cheetah conservation in these two countries is focussed primarily on the free-ranging populations as well as the mitigation of conflict between humans and cheetahs. In contrast, in neighbouring South Africa, the majority of cheetahs are found in fenced reserves. Nonetheless, conflict between humans and cheetahs remains an issue here. Conservation effort in South Africa is also focussed on managing the geographically isolated cheetah populations as one large meta-population. Relocation is one option among a suite of tools used to resolve human-cheetah conflict in southern Africa. Successfully relocating captured problem cheetahs, and maintaining a viable free-ranging cheetah population, are two environmental issues in cheetah conservation forming the first case study in this thesis.
The second case study involves the initiation of blooms of Lyngbya majuscula, a blue-green algae, in Deception Bay, Australia. L. majuscula is a toxic algal bloom which has severe health, ecological and economic impacts on the community located in the vicinity of this algal bloom. Deception Bay is an important tourist destination with its proximity to Brisbane, Australia’s third largest city. Lyngbya is one of several algae considered to be a Harmful Algal Bloom (HAB). This group of algae includes other widespread blooms such as red tides. The occurrence of Lyngbya blooms is not a local phenomenon, but blooms of this toxic weed occur in coastal waters worldwide. With the increase in frequency and extent of these HAB blooms, it is important to gain a better understanding of the underlying factors contributing to the initiation and sustenance of these blooms. This knowledge will contribute to better management practices and the identification of those management actions which could prevent or diminish the severity of these blooms.
43
De, Luca Giancarlo. "Decision Making in Complex Environments: an adaptive network approach." Doctoral thesis, SISSA, 2013. http://hdl.handle.net/20.500.11767/4808.
Full textAbstract:
In this thesis we investigate decision making in complex environments using
adaptive network models.
We first focus on the problem of consensus decision making in large animal
groups. Each individual has an internal state that models its choice among the
possible q alternatives and we assume that each individual updates its internal
state using a majority rule, if it is connected to other individuals, or using a
probabilistic rule. In this case, if the individual has no information, the choice
shall be totally random, otherwise the probabilistic rule shall have a bias toward
one of the q choices, measured by a parameter hi. The individuals shall also
update their neighbourhood adaptively, which is modelled by a link creation/
link destruction process with an effective rate z . We show that the system, if there
are no informed individuals, undergoes a I order phase transition at a give value,
∗z , between a disordered phase and a phase were consensus is reached. When
the number of informed individuals increases, the first order phase transition
remains, until one reaches a critical value of informed individuals above which
the system is no more critical. We also prove that, for z in a critical range, the
removal of knowledgeable individuals may induce a transition to a phase where
the group is no able to reach a consensual decision. We apply these results to
interpret some data on seasonal migrations of Atlantic Bluefin Tuna.
We, then, build a model to describe the emergence of hierarchical structures in
societies of rational self-interested agents. This model constitutes a highly stylised
model for human societies. The decision-making problem of the agents, in this
situation, is to which other agent to connect itself. We model the preference of
agents of that society for connecting to more prominent agents with a parameter
β. We show that there exists a sharp transition between a disordered equalitarian
society and an ordered hierarchical society as beta increases. Moreover, we
prove that, in a hierarchical society, social mobility is almost impossible, which
captures behaviours that have been observed in real societies.
44
Raimondo, Sebastian. "Network Models for Large-Scale Human Mobility." Doctoral thesis, Università degli studi di Trento, 2022. http://hdl.handle.net/11572/346543.
Full textAbstract:
Human mobility is a complex phenomenon emerging from the nexus between social, demographic, economic, political and environmental systems. In this thesis we develop novel mathematical models for the study of complex systems, to improve our understanding of mobility patterns and enhance our ability to predict local and global flows for real-world applications.The first and second chapters introduce the concept of human mobility from the point of view of complex systems science, showing the relation between human movements and their predominant drivers. In the second chapter in particular, we will illustrate the state of the art and a summary of our scientific contributions. The rest of the thesis is divided into three parts: structure, causes and effects.The third chapter is about the structure of a complex system: it represents our methodological contribution to Network Science, and in particular to the problem of network reconstruction and topological analysis. We propose a novel methodological framework for the definition of the topological descriptors of a complex network, when the underlying structure is uncertain. The most used topological descriptors are redefined – even at the level of a single node – as probability distributions, thus eluding the reconstruction phase. With this work we have provided a new approach to study the topological characteristics of complex networks from a probabilistic perspective.
The forth chapter deals with the effects of human mobility: it represents our scientific contribution to the debate about the COVID-19 pandemic and its consequences. We present a complex-causal analysis to investigate the relationship between environmental conditions and human activity, considered as the components of a complex socio-environmental system. In particular, we derive the network of relations between different flavors of human mobility data and other social and environmental variables. Moreover, we studied the effects of the restrictions imposed on human mobility – and human activities in general – on the environmental system. Our results highlight a statistically significant qualitative improvement in the environmental variable of interest, but this improvement was not caused solely by the restrictions due to COVID-19 pandemic, such as the lockdown.The fifth and sixth chapters deal with the modelling of causes of human mobility: the former is a concise chapter that illustrate the phenomenon of human displacements caused by environmental disasters. Specifically, we analysed data from different sources to understand the factors involved in shaping mobility patterns after tropical cyclones. The latter presents the Feature-Enriched Radiation Model (FERM), our generalization of the Radiation Model which is a state-of-the-art mathematical model for human mobility. While the original Radiation Model considers only the population as a proxy for mobility drivers, the FERM can handle any type of exogenous information that is used to define the attractiveness of different geographical locations. The model exploits this information to divert the mobility flows towards the most attractive locations, balancing the role of the population distribution. The mobility patterns at different scales can be reshaped, following the exogenous drivers encoded in the features, without neglecting the global configuration of the system.
45
Gwanvoma, Stephen B. "Systems Approach to Cross-Layer Optimization of a Complex Wireless Environment." International Foundation for Telemetering, 2011. http://hdl.handle.net/10150/595765.
Full textAbstract:
ITC/USA 2011 Conference Proceedings / The Forty-Seventh Annual International Telemetering Conference and Technical Exhibition / October 24-27, 2011 / Bally's Las Vegas, Las Vegas, NevadaThis paper presents a method for the optimization of mixed networks that incorporates a mixed layer optimization of performance features. The expanded integrated Network Enhanced Telemetry (iNET) system envisioned telemetering for large and complex networks which will require core telemetry networks with ad hoc extensions for coverage. Organizing such a network has been successfully accomplished in simulations using a K-mean clustering algorithm. This paper shows how the features of these network elements will be captured and disseminated in a real system. This management of network elements across multiple layers is characterized as cross-layer optimization. This paper will also show how such cross layer features can be combined for a globally optimum solution. It shows by example how the iNET system comprising multiple ground stations, gateways, frequency, nodes, and three performance measures can be optimized to achieve overall optimal system performance.
46
Rosvall, Martin. "Information horizons in a complex world." Doctoral thesis, Umeå : Department of Physics, Umeå University, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-840.
Full text
47
Olekas, Patrick T. "Characterization and Heuristic Optimization of Complex Networks." University of Cincinnati / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1224187184.
Full text
48
Ji, Haixia. "Uniqueness of Equilibria for Complex Chemical Reaction Networks." The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1307122057.
Full text
49
Viljoen, Nadia M. "Quantifying supply chain vulnerability using a multilayered complex network perspective." Thesis, University of Pretoria, 2018. http://hdl.handle.net/2263/63990.
Full textAbstract:
Today's supply chains face increasing volatility on many fronts. From the shop-floor where machines break and suppliers fail to the boardrooms where unanticipated price inflation erodes profi tability. Turbulence is the new normal.
To remain competitive and weather these (daily) storms, supply chains need to move away from an effi ciency mindset towards a resilience mindset. For over a little more than a decade industry and academia have awakened to this reality. Academic literature and case studies show that there is no longer a shortage of resilience strategies and designs. Unfortunately, industry still lacks the tools with which to assess and evaluate the effectiveness of such strategies and designs. Without the ability to quantify the benefi t it is impossible to motivate the cost.
This thesis adds one piece to the puzzle of quantifying supply chain vulnerability. Speci fically, it focussed on supply chains within urban areas. It addresses the question: "How does a supply chain's network design (internal con figuration) and its dependence on the underlying road network (external circumstances) make it more or less vulnerable to disruptions of the road network?"
Multilayered Complex Network Theory (CNT) held promise as a modelling approach that could capture the complexity of the dependence between a logical supply chain network and the physical road network that underpins it. This approach addressed two research gaps in complex network theory applications. In the supply chain arena CNT applications have reaped many benefi ts but the majority of studies regarded single-layer networks that model only supply chain relations. There were no studies found where the dependence of supply chain layers on underlying physical infrastructure was modelled in a multilayered manner. Road network applications offered many more multilayered applications but these primarily focussed on passenger transport, not freight transport.
The first artefact developed in the thesis was a multilayered complex network formulation representing a logical (supply chain) layer placed on a physical (road infrastructure) layer. The individual layers had predefi ned network characteristics and on their own could not hint at the inherent vulnerability that the system as a whole might have. From the multilayered formulation, the collection of shortest paths emerged. This is the collection of all shortest path alternatives within a network. The collection of shortest paths is the unique fingerprint of each multilayered network instance. The key to understanding vulnerability lies within the characteristics of the collection of shortest paths.
Three standard supply chain network archetypes were de fined namely the Fully Connected (FC), Single Hub (SH) and Double Hub (DH) archetypes. A sample of 500 theoretical multilayered network instances was generated for each archetype. These theoretical instances were subjected to three link-based progressive targeted disruption simulations to study the vulnerability characteristics of the collection of shortest paths. Two of the simulations used relative link betweenness to prioritise the disruptions while the third used the concept of network skeletons as captured by link salience. The results from these simulations showed that the link betweenness strategies were far more effective than the link salience strategy.
From these results three aspects of vulnerability were identifi ed. Redundancy quantifi es the number of alternative shortest paths available to an instance. Overlap measures to what degree the shortest path sets of an instance overlap and have road segments in common. Effi ciency step-change is a measure of the magnitude of the "shock" absorbed by the shortest paths of an instance during a disruption. For each of these aspects one or more metrics were defi ned. This suite of vulnerability metrics is the second artefact produced by the thesis.
The design of the artefacts itself, although novel, was not considered research. It is the insights derived during analysis of the artefacts' performance that contributes to the body of knowledge. Link-based progressive random disturbance simulations were used to assess the ability of the vulnerability metrics to quantify supply chain vulnerability. It was found that none of the de fined vulnerability aspects are good stand-alone predictors of vulnerability. The multilayered nature and random disturbance protocol result in vulnerability being more multi-faceted than initially imagined. Nonetheless, the formulation of the multilayered network proved useful and intuitive and even though the vulnerability metrics fail as predictors they still succeed in capturing shortest path phenomena that would lead to vulnerability under non-random protocols.
To validate the fi ndings from the theoretical instances, link-based random disturbance simulations were executed on 191 case study instances. These instances were extracted from real-life data in three urban areas in South Africa, namely Gauteng Province (GT), City of Cape Town (CoCT) and eThekwini Metropolitan Municipality (ET). The case study instances showed marked deviations from the assumptions underlying the theoretical instances. Despite these differences, the multilayered formulation still enables the quanti fication of the relationship between supply chain structure and road infrastructure. The performance of the vulnerability metrics in the case study corroborates the findings from the theoretical instances.
Although the suite of vulnerability metrics was unsuccessful in quantifying or predicting vulnerability in both the theoretical and case study instances, the rationale behind their development is sound. Future work that will result in more effective metrics is outlined in this thesis. On the one hand the development of a more realistic disruption strategy is suggested. Road network disruptions are neither completely random nor specifi cally targeted. Important segments with greater tra ffic loads are more likely to be disrupted, but the reality is that disruptions such as accidents, equipment failure or road maintenance could really occur anywhere on the network. A more realistic disruption strategy would lie somewhere on the continuum between targeted and random disruptions. Other future work suggests the refi nement of both artefacts by incorporating link
weights in both the logical and physical layers.
An unanticipated fi nding from this thesis is that future research in the fi eld may be expedited if theory-building emanates from real-life empirical networks as opposed to theoretically generated networks. Expanding the scope of the case study, characterising the true network archetypes found in practice and increasing the number of case study samples is a high priority for future work.Thesis (PhD)--University of Pretoria, 2018.
National Research Foundation of South Africa (Grant UID: 105519). Partial funding of doctoral research.
Industrial and Systems Engineering
PhD
Unrestricted
50
Martorello, Cristiane Dias de Souza. "Epidemiology in complex networks - modified heterogeneous mean-field model." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/100/100132/tde-16012019-173906/.
Full textAbstract:
The study of complex networks presented a huge development in last decades. In this dissertation we want to analyze the epidemic spread in scale-free networks through the Susceptible - Infected - Susceptible (SIS) model. We review the fundamental concepts to describe complex networks and the classical epidemiological models. We implement an algorithm that produces a scale-free network and explore the Quenched Mean-Field (QMF) dynamics in a scale-free network. Moreover, we simulate a change on the topology of the network according to the states of the nodes, and it generates a positive epidemic threshold. We show analytically that the fraction of infected vertices follows a power-law distribution in the vicinity of this critical pointO estudo de redes complexas tem se desenvolvido muito nos últimos anos. Nesta dissertação queremos analisar o processo de propagação de epidemia em redes livres de escala através do modelo Suscetível - Infectado - Suscetível (SIS). Apresentamos uma revisão de redes e as principais características dos modelos epidemiológicos clássicos. Implementamos um algoritmo que produz uma rede livre de escala dado um expoente e exploramos a dinâmica do modelo Quenched Mean-Field (QMF) inserido em uma rede livre de escala. Além disso, foi simulada uma possível alteração na topologia da rede, devido aos estados dos vértices infectados, que gerou um limiar epidêmico positivo no modelo e a probabilidade de vértices infectados seguiu uma lei de potência na vizinhança desse ponto crítico