Δίκτυα που εμφανίζουν μόνιμα μια συγκεκριμένη ιδιότητα ανεξάρτητα από το μέγεθος και την πυκνότητά τους ονομάζονται ανεξάρτητα από την κλίμακα (scale-free). Σε πολλά πραγματικά δίκτυα αυτή η ιδιότητα ταυτίζεται με την κατανομή των βαθμών των κόμβων σύμφωνα με τον νόμο της δύναμης με εκθέτη στο διάστημα [2..4]. Η εργασία παρουσιάζει τρία στατικά μοντέλα κατασκευής scale-free δικτύων με την παραπάνω ιδιότητα, βασισμένα στο δυναμικό μοντέλο Barabási-Albert, και επιχειρεί να προσεγγίσει πειραματικά τον χρωματικό τους αριθμό. / Networks that exhibit a certain quality irrespective of their size and density are called scale-free. In many real-life networks this quality coincides with a power-law distribution of the nodes' degree with exponent ranging in [2..4]. This work presents three static models for constructing scale-free networks, based on the dynamic Barabási-Albert model, and attempts to experimentally approximate their chromatic number.

No description available.

In this work we analyse two problems coming from theoretical biology. In the first part we propose a spatially structured population model which is defined on a continuous lattice. In the model individuals disperse at a constant rate v and competition is local and delimitated by the competition radius R. Due to dispersal, the neighborhooh size fluctuates over time. We analyse how these variables affect the adaptive process. While the fixation probabilities of beneficial mutations are roughly the same as in a panmitic population for small and intermediate fitness effects s, a dependence on v and R appears for large s. These quantities also strongly influence fixation times. The model exhibits a dual behavior displaying a power-law growth for the fixation rate and speed of adaptation with the beneficial mutation rate as observed in spatially structured population models, but simultaneously showing a non-saturating behavior for the speed of adaptation with the population size. In the second part we numerically study the dynamics of model imune networks with random and scale-free topologies. We observe that a memory state is reached when the antigen is attached to the most connected sites of the network, where as a percolation state may occur when the antigen attaches to the less connected sites. For increasing values of the connectivity, its population converges exponentially to the asymptotic value of the memory state. On the other hand, the next-nearest populations evolve slowly as power-laws towards the virgin-like state. / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Neste trabalho, analisamos dois problemas provenientes da biologia teórica. Na primeira parte, propomos um modelo de população espacialmente estruturada, que é definido numa rede contínua. No modelo, indivíduos se dispersam numa taxa constante v e a competição é local e delimitada pelo raio de competição R. Devido à dispersão, o tamanho da vizinhança flutua ao longo do tempo. Analisamos como essas variáveis afetam o processo adaptativo. Embora as probabilidades de fixação de mutações benéficas sejam aproximadamente as mesmas que numa população panmítica para valores de adaptação de pequeno e médio s, uma dependência de v e R aparece para grandes s. Estas quantidades também influenciam fortemente os tempos de fixação. O modelo exibe um comportamento duplo que indica um crescimento em lei de potência para a taxa de fixação e a velocidade de adaptação com a taxa de mutação benéfica como observado em modelos de população espacialmente estruturadas, mas simultaneamente mostra um comportamento não saturante para a velocidade de adaptação com o tamanho da população. Na segunda parte, estudamos numericamente a dinâmica de modelos de redes imunes com topologias aleatória e livre de escala. Observamos que um estado memória é alcançado quando o antígeno é ligado aos sítios mais conectados da rede enquanto que um estado de percolação pode ocorrer quando o antígeno se liga aos sítios menos conectados. Para maiores valores de conectividade, sua população converge exponencialmente para o valor assintótico do estado de memória. Por outro lado, as populações mais próximas evoluem lentamente, como leis de potência para o estado virgem.

Este trabalho explora o uso da Análise de Redes Sociais (ARS) como ferramenta de grande valor, aquela que perpassa pelas mais variadas disciplinas, como protagonista do estudo em alguns casos, como coadjuvante em outros. Para a Ciência da Informação, a ARS vem sendo empregada em estudos bibliométricos, procurando responder a questionamentos que intrigam pesquisadores da área ou de outros segmentos do conhecimento. Assim, a ARS ocupa seu espaço como o objeto principal dos estudos, enfatizando-a como ferramenta e, também, pelo seu uso direto em pesquisas nas quais aparece, frequentemente em conjunto com a estatística. Dessa forma, a ARS é empregada, pensando em Ciência da Informação, em ambos os papéis: como protagonista, nas frentes que abordam sua história, seus métodos e suas métricas, e, como coadjuvante, contribuindo na análise de redes de coautoria através de suas métricas de centralidade, mostrando a fluência das informações, determinando a posição de autores com relação à colaboração e seus comportamentos em rede para áreas como a Ciência da Informação, a Educação e a Sociologia. Os gráficos e tabelas foram elaborados com o apoio dos programas Microsoft Excel e, fundamentalmente para as redes de interesse, o programa de uso livre Pajek. Este último, alimentado por programas em VBScript, possibilitou, através de seus recursos de geração de imagens representativas das redes, a confecção dos gráficos e o cálculo dos indicadores para cada uma das três redes. Para a obtenção dos dados de entrada, foram utilizados os mecanismos de busca pela expressão \"Social Networks\" nas bases ASSIA (Sociologia), ERIC (Educação) e LISA (Ciência da Informação) e, com o uso de seus mecanismos internos, a massa foi exportada e empregada como exemplo de bases viabilizando a aplicação e verificação da metodologia proposta nos moldes dos estudos realizados. / This dissertation explores the use of Social Network Analysis (SNA) as a valuable tool, which runs through the most varied disciplines, as a protagonist in same study cases, as an adjunct in other cases. For the Information Science, the SNA has been used in bibliometric studies, trying to answer questions that intrigue researchers in this field or other segments of knowledge. So, the SNA occupies its place as the main object of the studies, emphasizing it as a tool and also for its direct use in researches in which it appears, often in conjunction with the statistics. Thus, the SNA is used, concerning the Information Science, in both roles: as the protagonist, foremost addressing its history, its methods and metrics, and, as an adjunct, contributing for the analysis of networks of co-authorship through its centrality metrics, showing the flow of information, determining the position of the authors, related to their collaboration and their behaviors on the network, for areas of study such as Information Science, Education and Sociology. The graphs and charts were elaborated with the support of Microsoft Excel program and, fundamentally to the networks of interest, the program of free use called Pajek. The latter, powered by VBScript programs, enabled, through its resources of generating representative network images, the elaboration of the graphs and the calculation of the indicators for each one of the three networks. To obtain the input data, search engines were used by the expression \"Social Networks\" in ASSIA (Sociology), ERIC (Education) and LISA (Information Science) bases and, using its internal mechanisms, the mass was exported and used as an example of bases enabling the application and verification of the proposed methodology along the lines of the studies.

No description available.

京都大学 / 新制・課程博士 / 博士(情報学) / 甲第24041号 / 情博第797号 / 新制||情||135(附属図書館) / 京都大学大学院情報学研究科システム科学専攻 / (主査)教授 加納 学, 教授 下平 英寿, 教授 石井 信 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

No description available.

Phan (2003)等人的研究首先採用網路結構的觀點，來進行在獨占市場下的消費行為分析。本文延伸Phan等人的研究，採用多樣性的不同網路結構，特別是以「無標度網路」(scale-free)作為市場背後的網路結構，藉此來探討網路對於市場需求行為的影響。同時，為了有效地進行量化分析，我們建立了一些數學公式，以便能夠精準地比較不同網路對於需求面的影響程度，其中包含了消費者剩餘、雪崩效果和磁滯效果等。在實驗結果中，我們發現網路結構的確會影響到消費行為的表現，並且當市場規模變大時，仍存在著一些因網路不同而有的差異性。 / The economic implications of network topologies are studied via a monopolist's model of market networks originally proposed by Phan, et al. (2003). By embedding the market into a larger collection of network topologies, in particular, a class of scale-free networks, we extend the early analysis built upon a class of ring networks. To facilitate the study of the impacts of network topologies upon market demand, various measures concerning social welfare (the consumer's surplus), the avalanche effect, and the hysteresis effect, are formally established. Comparisons based on these measures show that network topologies matter, and their implied differences will remain even when the network size becomes large.

Real-world large-scale complex networks such as the Internet, social networks and biological networks have increasingly attracted the interest of researchers from many areas. Accurate modelling of the statistical regularities of these large-scale networks is critical to understand their global evolving structures and local dynamical patterns. Traditionally, the Erdos and Renyi random graph model has helped the investigation of various homogeneous networks. During the past decade, a special computational methodology has emerged to study complex networks, the outcome of which is identified by two models: the Watts and Strogatz small-world model and the Barabasi-Albert scale-free model. At the core of the complex network modelling process is the extraction of characteristics of real-world networks. I have developed computer simulation algorithms for study of the properties of current theoretical models as well as for the measurement of two real-world complex networks, which lead to the isolation of three complex network modelling essentials. The main contribution of the thesis is the introduction and study of a new General Two-Stage growth model (GTS Model), which aims to describe and analyze many common-featured real-world complex networks. The tools we use to create the model and later perform many measurements on it consist of computer simulations, numerical analysis and mathematical derivations. In particular, two major cases of this GTS model have been studied. One is named the U-P model, which employs a new functional form of the network growth rule: a linear combination of preferential attachment and uniform attachment. The degree distribution of the model is first studied by computer simulation, while the exact solution is also obtained analytically. Two other important properties of complex networks: the characteristic path length and the clustering coefficient are also extensively investigated, obtaining either analytically derived solutions or numerical results by computer simulations. Furthermore, I demonstrate that the hub-hub interaction behaves in effect as the link between a network's topology and resilience property. The other is called the Hybrid model, which incorporates two stages of growth and studies the transition behaviour between the Erdos and Renyi random graph model and the Barabasi-Albert scale-free model. The Hybrid model is measured by extensive numerical simulations focusing on its degree distribution, characteristic path length and clustering coefficient. Although either of the two cases serves as a new approach to modelling real-world large-scale complex networks, perhaps more importantly, the general two-stage model provides a new theoretical framework for complex network modelling, which can be extended in many ways besides the two studied in this thesis.

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.