abstract: The power of science lies in its ability to infer and predict the existence of objects from which no direct information can be obtained experimentally or observationally. A well known example is to ascertain the existence of black holes of various masses in different parts of the universe from indirect evidence, such as X-ray emissions. In the field of complex networks, the problem of detecting hidden nodes can be stated, as follows. Consider a network whose topology is completely unknown but whose nodes consist of two types: one accessible and another inaccessible from the outside world. The accessible nodes can be observed or monitored, and it is assumed that time series are available from each node in this group. The inaccessible nodes are shielded from the outside and they are essentially ``hidden.'' The question is, based solely on the available time series from the accessible nodes, can the existence and locations of the hidden nodes be inferred? A completely data-driven, compressive-sensing based method is developed to address this issue by utilizing complex weighted networks of nonlinear oscillators, evolutionary game and geospatial networks. Both microbes and multicellular organisms actively regulate their cell fate determination to cope with changing environments or to ensure proper development. Here, the synthetic biology approaches are used to engineer bistable gene networks to demonstrate that stochastic and permanent cell fate determination can be achieved through initializing gene regulatory networks (GRNs) at the boundary between dynamic attractors. This is experimentally realized by linking a synthetic GRN to a natural output of galactose metabolism regulation in yeast. Combining mathematical modeling and flow cytometry, the engineered systems are shown to be bistable and that inherent gene expression stochasticity does not induce spontaneous state transitioning at steady state. By interfacing rationally designed synthetic GRNs with background gene regulation mechanisms, this work investigates intricate properties of networks that illuminate possible regulatory mechanisms for cell differentiation and development that can be initiated from points of instability. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2015

Submitted by ALECIANA VASCONCELOS ORTEGA null (aleciana@gmail.com) on 2017-09-26T14:16:38Z No. of bitstreams: 1 tese completa.pdf: 2429935 bytes, checksum: d3859048243f82a938ffc7deb29377f0 (MD5) / Approved for entry into archive by Monique Sasaki (sayumi_sasaki@hotmail.com) on 2017-09-28T12:47:55Z (GMT) No. of bitstreams: 1 ortega_av_dr_ilha.pdf: 2429935 bytes, checksum: d3859048243f82a938ffc7deb29377f0 (MD5) / Made available in DSpace on 2017-09-28T12:47:55Z (GMT). No. of bitstreams: 1 ortega_av_dr_ilha.pdf: 2429935 bytes, checksum: d3859048243f82a938ffc7deb29377f0 (MD5) Previous issue date: 2017-09-01 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / As Redes Complexas podem descrever vários tipos de sistemas importantes através da representação dos grafos. Com o aumento da capacidade de processamento e armazenamento dos computadores, tornou-se possível o acesso e a análise de várias bases de dados de diversas áreas, o que permitiu a comparação de redes do mundo real com os modelos de redes já existentes. Essas redes complexas apresentam propriedades que são úteis nas análises dos mais diversos aspectos das redes e com os mais variados propósitos. O presente trabalho tem por objetivo investigar as propriedades estruturais e de funcionamento das redes de distribuição de energia, considerando suas diferentes topologias, a fim de definir um modelo através do uso de Redes Complexas com o propósito de analisar o comportamento da rede, considerando, características de desempenho, resiliência e identificação de falhas. Nesta tese foi utilizada uma rede padrão chamada de Rede Modelo de distribuição de energia elétrica a qual foi modelada e simulada para servir de referência para comparar as métricas da Rede de Distribuição da cidade de Ilha Solteira. Nestes modelos analisados, os transformadores referem-se aos vértices da rede enquanto que as ligações entre eles representam as arestas do grafo. Um fato importante na utilização dos modelos é a possibilidade de estudar e detectar qualquer característica dos relacionamentos e assim direcionar recursos para uma proposta. Os alimentadores analisados em questão comparados a rede padrão apresentada, tiveram um desempenho bom no que diz respeito a sua aplicação que puderam ser demonstrados através de suas propriedades ou métricas. O modelo adotado forneceu a característica real das redes analisadas. Pode-se perceber que conforme as redes aumentam o tamanho, o número de vértices se torna de baixo grau, o que possibilita o crescimento desta rede. Os estudos desenvolvidos nesta tese se tornam muito relevantes porque a análise da rede complexa permite a operadores e engenheiros praticidade na operação e gerência da rede, bem como, planejamento e otimização no uso de seus recursos. / Complex Networks can describe several types of important systems through the representation of graphs. With the increase in the processing and storage capacity of the computers, it became possible to access and analyze several databases from different areas, which allowed the comparison of real-world networks with existing network models. These complex networks present properties that are useful in the analysis of the most diverse aspects of the networks and for the most varied purposes. The objective of this work is to investigate the structural and operational properties of the energy distribution networks, considering their different topologies, in order to define a model through the use of Complex Networks with the purpose of analyzing the network behavior, considering characteristics Performance, resiliency, and fault identification. In this thesis was used a standard network called Model Electric Distribution Network which was modeled and simulated to serve as reference to compare the metrics of the Distribution Network of the city of Ilha Solteira. In these analyzed models, the transformers refer to the vertices of the network while the connections between them represent the edges of the graph. An important fact in the use of models is the possibility of studying and detecting any characteristic of relationships and thus directing resources to a proposal. The analyzed feeders in question compared to the standard network presented, had a good performance with respect to their application that could be demonstrated through their properties or metrics. The adopted model provided the real characteristic of the analyzed networks. It can be noticed that as the networks increase in size, the number of vertices becomes low-grade, which allows the growth of this network. The studies developed in this thesis become very relevant because the analysis of the complex network allows to the operators and engineers practicality in the operation and management of the network, as well as, planning and optimization in the use of its resources.

Submitted by EDUARDO RIBEIRO PINTO (eduribeiro2@bol.com.br) on 2018-05-03T15:47:28Z No. of bitstreams: 1 dissertacao_Eduardo.pdf: 6068904 bytes, checksum: 4ff00adcd4667c6d7ed4bcfb5db2321a (MD5) / Approved for entry into archive by Sulamita Selma C Colnago null (sulamita@btu.unesp.br) on 2018-05-03T19:01:49Z (GMT) No. of bitstreams: 1 pinto_er_me_bot_int.pdf: 6068904 bytes, checksum: 4ff00adcd4667c6d7ed4bcfb5db2321a (MD5) / Made available in DSpace on 2018-05-03T19:01:49Z (GMT). No. of bitstreams: 1 pinto_er_me_bot_int.pdf: 6068904 bytes, checksum: 4ff00adcd4667c6d7ed4bcfb5db2321a (MD5) Previous issue date: 2018-02-23 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Os Modelos Baseados em Indivíduos (MBI’s) têm sido crescentemente empregados na modelagem de processos infecciosos. Um MBI consiste de uma estrutura na qual ocorrem interações entre um certo número de indivíduos, cujo comportamento é determinado por um conjunto de características que evoluem estocasticamente no tempo. Estudos recentes têm mostrado que as redes complexas constituem um suporte natural para o estudo da propagação de uma doença. Redes complexas são descritas por um conjunto de vértices (nós), arestas (conexões, ligações ou links) e algum tipo de interação entre os mesmos. Na formulação original do MBI e em modelos SIR (Suscetível, Infectado e Recuperado) e SEI (Suscetível, Exposto e Infectado), as relações entre os indivíduos são representadas por grafos completos, ou seja, todos os indivíduos estão conectados entre si. Como a topologia de uma rede real não pode ser descrita por uma rede puramente aleatória, nesse trabalho o MBI foi implementado de forma a incorporar modelos mais realísticos de redes de contato na propagação de uma doença infecciosa. De maneira geral, observou-se que redes complexas com diferentes topologias resultam em curvas de indivíduos suscetíveis, infectados e recuperados (ou suscetíveis, expostos e infectados) com diferentes comportamentos, e desta forma, que a evolução de uma dada doença, em particular a tuberculose, é altamente sensível à topologia de rede utilizada. Mais especificamente, observou-se que quanto maior o valor do comprimento do salto médio, mais rápida será a propagação da doença e, consequentemente, maior será o número de indivíduos infectados. / Individual-Based Models have been increasingly employed in the modeling of an infectious process. An IBM consists of a structure in which interactions occur between a certain number of individuals, whose behavior is determined by a set of characteristics that evolve stochastically in time. Recent studies have shown that complex networks are a natural framework for the study of a disease spread. Complex networks are described by a set of vertices (or nodes), edges (connections or links) and some type of interactions between them. In the original IBM approach and in SIR (Susceptible, Infected, Recovered) and SEI (Susceptible, Exposed and Infected) models, the relations between individuals are represented by complete graphs, that is, all individuals are connected to each other. Since the topology of a real network can not be described by a purely random network, in this work an IBM has been implemented in order to incorporate some realistic contact networks xvii models. In general, it was observed that complex networks with different topologies correspond to curves of susceptible, infected and recovered individuals (or susceptible, exposed and infected) with different behaviors, and thus, that the evolution of a given disease, in particular tuberculosis, is highly sensitive to a network topology. More specifically, it was observed that the higher the value of the mean jump length is, the faster the disease spreads and consequently, the higher is the number of infected individuals.

Made available in DSpace on 2015-03-03T15:15:29Z (GMT). No. of bitstreams: 1 FranciscoBLJ_DISSERT.pdf: 1340368 bytes, checksum: 23cf640d31d17bdd88ad96134433ceb1 (MD5) Previous issue date: 2013-11-29 / We studied the Ising model ferromagnetic as spin-1/2 and the Blume-Capel model as spin-1, > 0 on small world network, using computer simulation through the Metropolis algorithm. We calculated macroscopic quantities of the system, such as internal energy, magnetization, specific heat, magnetic susceptibility and Binder cumulant. We found for the Ising model the same result obtained by Koreans H. Hong, Beom Jun Kim and M. Y. Choi [6] and critical behavior similar Blume-Capel model / ?Neste trabalho estudamos o modelo de Ising ferromagn?tico com spin-1/2 e o modelo Blume-Capel com spin-1, ? > 0 em rede mundo pequeno, usando simula??o computacional atrav?s do algoritmo de Metropolis. Calculamos grandezas macrosc?picas do sistema, tais como a energia interna, a magnetiza??o, o calor espec?fico, a susceptibilidade magn?tica e o cumulante de Binder. Encontramos para o modelo de Ising o mesmo resultado obtido pelos Coreanos H. Hong, Beom Jun Kim e M. Y. Choi [6] e um comportamento cr?tico similar o modelo Blume-Capel.

Esse trabalho consiste em um estudo sobre a aplicabilidade da medida de centralidade de intermediação (betweenness centrality) combinada com demandas de tráfego em redes viárias com o objetivo de identificar os principais pontos dessas redes. Como principais pontos refere-se aqui aos que aparecem com maior frequência entre os caminhos utilizados pelos motoristas que se deslocam pela rede viária. Trata-se de um estudo exploratório, que se inicia com a aplicação da centralidade de intermediação sobre redes simples, estendendo-se até simulações sobre redes baseadas em mapas reais. Nesse trabalho é analisado o comportamento da medida de centralidade sobre a topologia da rede - i.e. tanto sem considerar uma demanda, como considerando demandas de diversas magnitudes. Para ilustrar a proposta, os resultados são confrontados com valores de centralidade de intermediação calculados sobre as taxas de ocupação das vias extraídas de simulação microscópica. Ao final, foram apresentadas evidências de que o método proposto consegue explicar os fluxos de tráfego com melhor desempenho do que a centralidade de intermediação original. No entanto, o método mostrou-se muito sensível à função de custo utilizada na atribuição da demanda de tráfego ao grafo da rede. Os melhores resultados demonstrados pela abordagem proposta foram obtidos em experimentos sobre redes não regulares e com demandas de tráfego não uniformes. No caso de redes regulares com demanda uniforme, o melhor desempenho foi obtido pelo cálculo da centralidade sem considerar a demanda, mas atribuindo-se o custo unitário às arestas do grafo representativo da rede. / This work consists of a study of applicability of betweenness centrality combined with traffic demands in road networks with the objective of identifying their central points. By central points we refer to those which appear with high frequency among the paths used by drivers that move along the road network. It is an exploratory study, which begins with the application of the betweenness centrality on simple networks, extending to simulations on networks based on real maps. In this study we have analyzed the behavior of the metric over the network topology - i.e. without considering demand, as well as experiments considering demands with several magnitudes. To illustrate the proposed method, the results are compared with betweenness centrality values calculated over roadways occupation rates extracted from microscopic simulation. At the end, evidence that the proposed method can explain traffic flows with better performance than the original betweenness centrality were presented. However, the proposed method was shown to be very sensitive to the cost function used in the allocation of the graph network traffic demand. The best results demonstrated by the proposed approach were obtained in experiments on nonregular networks and non-uniform traffic demands. In the case of regular networks with uniform demand, the best performance was obtained by calculating the betweenness centrality without considering the demand, but assigning the unitary cost to the edges of the network graph.

abstract: What can classical chaos do to quantum systems is a fundamental issue highly relevant to a number of branches in physics. The field of quantum chaos has been active for three decades, where the focus was on non-relativistic quantumsystems described by the Schr¨odinger equation. By developing an efficient method to solve the Dirac equation in the setting where relativistic particles can tunnel between two symmetric cavities through a potential barrier, chaotic cavities are found to suppress the spread in the tunneling rate. Tunneling rate for any given energy assumes a wide range that increases with the energy for integrable classical dynamics. However, for chaotic underlying dynamics, the spread is greatly reduced. A remarkable feature, which is a consequence of Klein tunneling, arise only in relativistc quantum systems that substantial tunneling exists even for particle energy approaching zero. Similar results are found in graphene tunneling devices, implying high relevance of relativistic quantum chaos to the development of such devices. Wave propagation through random media occurs in many physical systems, where interesting phenomena such as branched, fracal-like wave patterns can arise. The generic origin of these wave structures is currently a matter of active debate. It is of fundamental interest to develop a minimal, paradigmaticmodel that can generate robust branched wave structures. In so doing, a general observation in all situations where branched structures emerge is non-Gaussian statistics of wave intensity with an algebraic tail in the probability density function. Thus, a universal algebraic wave-intensity distribution becomes the criterion for the validity of any minimal model of branched wave patterns. Coexistence of competing species in spatially extended ecosystems is key to biodiversity in nature. Understanding the dynamical mechanisms of coexistence is a fundamental problem of continuous interest not only in evolutionary biology but also in nonlinear science. A continuous model is proposed for cyclically competing species and the effect of the interplay between the interaction range and mobility on coexistence is investigated. A transition from coexistence to extinction is uncovered with a non-monotonic behavior in the coexistence probability and switches between spiral and plane-wave patterns arise. Strong mobility can either promote or hamper coexistence, while absent in lattice-based models, can be explained in terms of nonlinear partial differential equations. / Dissertation/Thesis / Ph.D. Electrical Engineering 2012

La notion de communauté, transverse à  l'analyse des réseaux sociaux, a attiré une attention grandissante à  travers les sciences ces dix dernières années. Les nombreuses tentatives pour modéliser aussi bien l'incarnation sociologiquedu concept aussi bien que sa manifestation structurelle dans le réseau social n'ont jusqu'à  présent que vaguement convergé. Aucun consensus formel n'a été atteint sur les aspects quantifiables de la communauté, et ceci malgré lesliens forts la reliant aux dimensions dynamique et topologique du réseau sous-jacent.Présentant une approche novatrice à  l'évaluation des communautés, cette thèse introduit et se base sur la cohésion, une métrique qui capture la qualitéintrinsèque, en tant que communauté, d'un ensemble de sommets dans un réseau. Il a été montré au travers d'une experience à  large échelle, dans laquelle les individus sondés ont pu noter l'aspect communautaires de groupes d'amis leur étant présentés, que la cohésion, définie en lien avec la notion de triades sociales, est fortement correlée à  la perception subjective de la communauté. Reflétant la complexité des interactions sociales, il est démontré que leproblème de trouver des communautés maximalement cohésive est NP-dur. En utilisant une heuristique approximant les résultats de ce problème, un certain nombre d'applications de la cohésion à  des données réelles sont mises en avant: de son application à  la visualisation de réseaux complexes, à  l'étude de l'évolution des groupes d'agrément du sénat états-unien, à  la compréhesion des liens entre psychologie et structure du réseau social.L'utilisation de la cohésion apporte un éclairage non trivial dans l'étude de la structure des grands réseaux de terrain et dans la relation entre structure et sémantique. / Community, a notion transversal to all areas of Social Network Analysis, has drawn tremendous amount of attention across the sciences in the past decades. Numerous attempts to characterize both the sociological embodiment of the concept as well as its observable structural manifestation in the social network have to this date only converged in spirit. No formal consensus has been reached on the quantifiable aspects of community, despite it being deeply linked to topological and dynamic aspects of the underlying social network. Presenting a fresh approach to the evaluation of communities, this thesis introduces and builds upon the cohesion, a novel metric which captures the intrinsic quality, as a community, of a set of nodes in a network. The cohesion, defined in terms of social triads, was found to be highly correlated to the subjective perception of communitiness through the use of a large-scale online experiment in which users were able to compute and rate the quality of their social groups on Facebook. Adequately reflecting the complexity of social interactions, the problem of finding a maximally cohesive group inside a given social network is shown to be NP-hard. Using a heuristic approximation algorithm, applications of the cohesion to broadly different use cases are highlighted, ranging from its application to network visualization, to the study of the evolution of agreement groups in the United States Senate, to the understanding of the intertwinement between subjects' psychological traits and the cohesive structures in their social neighborhood. The use of the cohesion proves invaluable in that it offers non-trivial insights on the network structure and its relation to the associated semantic.

Les réseaux sont présents dans pratiquement tous les aspects de la vie. Le monde quinous entoure comporte énormément de réseaux. Par exemple, les réseaux de communicationconstitués de téléphones, les réseaux électriques, les réseaux d’ordinateurs, le réseaudes lignes aériennes, ... etc, sont autant de réseaux importants dans la vie de chaque jour.Le cadre mathématique des réseaux est bien approprié pour décrire plusieurs systèmescomposés d’un grand nombre d’entités qui interagissent entre elles. Chaque entité est représentéepar un noeud du réseau et chaque interaction par un lien entre deux noeuds. Ilest donc possible de modéliser ces réseaux par des graphes. Pour la plupart de ces réseaux,la difficulté provient principalement du grand nombre d’entités, ainsi que de la façon dontelles sont interconnectées. Une approche naturelle pour simplifier de tels systèmes consistedonc à réduire leur taille. Cette simplification n’est pas faite aléatoirement, mais de tellefaçon à ce que les noeuds de la même composante aient plus de liens entre eux qu’avec lesautres composantes. Ces groupes de noeuds ou composantes sont appelés communautésd’intérêt. Notre thèse se positionne dans le domaine de l’étude des graphes sociaux. Elle s’intéresseprincipalement à l’étude de la robustesse des structures sociales émergentes dansles réseaux d’interactions. L’aspect de la robustesse des réseaux constitue un challengetrès important pour comprendre leur fonctionnement, le comportement des entités lesconstituant et surtout pour comprendre les interactions qui peuvent se produire entreelles, permettant l’émergence de certains comportements qui n’étaient pas du tout prévisiblesau préalable. Actuellement, les études de la robustesse des réseaux qui existentdans la littérature traitent cet aspect du point de vue purement structurel, i.e. toutes lesperturbations sont appliquées soit sur les noeuds, soit sur les arêtes du graphe. Pour cequi est de notre étude, nous nous sommes intéressés à définir une nouvelle stratégie qui sebase sur des perturbations appliquées sur les paramètres qui permettent l’émergence desgraphes sociaux dans les réseaux d’interaction. Cette façon d’aborder l’aspect robustessedes graphes constitue une nouvelle manière d’évaluer et de quantifier les changements quipeuvent intervenir dans les structures de ces graphes. / Networks are present in virtually all aspects of life. The world surrounding usincludes to many networks. For example, communication networks constituted of phones,electrical networks, computers networks, aerial lines network, ? etc, are such importantnetworks in our daily life. The mathematical framework of networks is well appropriatedto describe different systems composed of many entities interacting with each other. Eachentity is represented by a network node and each interaction by a link between twonodes. Therefore, it is possible to model these networks by graphs. For most of thesenetworks, the difficulty comes mainly from the large number of entities and the way theyare interconnected. A natural approach to simplify such systems is therefore to reducetheir size. This simplification is not made randomly, but in such a way that the nodes ofthe same component would have more connections between themselves than with othercomponents. These groups of nodes or components are called communities of interest.Our thesis is positioned in the field of social graphs study. It is mainly interested instudying the robustness of social structures emerging in interaction networks. The aspectof networks robustness is a very important challenge to understand their functioning,the behavior of the constituting entities and especially to understand the interactionsthat may occur between them, allowing the emergence of certain behaviors that were notpredictable at all in advance. Currently, studies of networks robustness that exist in theliterature treat this aspect from a purely structural point of view, ie, all perturbations areapplied either on nodes or on the edges of the graph. In terms of our study, we focused ondefining a new strategy based on perturbations applied on the parameters that allow theemergence of social graphs in interaction networks. This way to approach the robustnessappearance of the graphs is a new way to assess and quantify the changes that may occurin the structures of these graphs.

Complex networks have been successfully applied to different fields, being the subject of study in different areas that include, for example, physics and computer science. The finding that methods of complex networks can be used to analyze texts in their different complexity levels has implied in advances in natural language processing (NLP) tasks. Examples of applications analyzed with the methods of complex networks are keyword identification, development of automatic summarizers, and authorship attribution systems. The latter task has been studied with some success through the representation of co-occurrence (or adjacency) networks that connect only the closest words in the text. Despite this success, only a few works have attempted to extend this representation or employ different ones. Moreover, many approaches use a similar set of measurements to characterize the networks and do not combine their techniques with the ones traditionally used for the authorship attribution task. This Masters research proposes some extensions to the traditional co-occurrence model and investigates new attributes and other representations (such as mesoscopic and named entity networks) for the task. The connectivity information of function words is used to complement the characterization of authors writing styles, as these words are relevant for the task. Finally, the main contribution of this research is the development of hybrid classifiers, called labelled motifs, that combine traditional factors with properties obtained with the topological analysis of complex networks. The relevance of these classifiers is verified in the context of authorship attribution and translationese identification. With this hybrid approach, we show that it is possible to improve the performance of networkbased techniques when they are combined with traditional ones usually employed in NLP. By adapting, combining and improving the model, not only the performance of authorship attribution systems was improved, but also it was possible to better understand what are the textual quantitative factors (measured through networks) that can be used in stylometry studies. The advances obtained during this project may be useful to study related applications, such as the analysis of stylistic inconsistencies and plagiarism, and the analysis of text complexity. Furthermore, most of the methods proposed in this work can be easily applied to many natural languages. / Redes complexas vem sendo aplicadas com sucesso em diferentes domínios, sendo o tema de estudo de distintas áreas que incluem, por exemplo, a física e a computação. A descoberta de que métodos de redes complexas podem ser utilizados para analisar textos em seus distintos níveis de complexidade proporcionou avanços em tarefas de processamento de línguas naturais (PLN). Exemplos de aplicações analisadas com os métodos de redes complexas são a detecção de palavras-chave, a criação de sumarizadores automáticos e o reconhecimento de autoria. Esta última tarefa tem sido estudada com certo sucesso através da representação de redes de co-ocorrência (ou adjacência) de palavras que conectam apenas as palavras mais próximas no texto. Apesar deste sucesso, poucos trabalhos tentaram estender essas redes ou utilizar diferentes representações. Além disso, muitas das abordagens utilizam um conjunto semelhante de medidas de redes complexas e não combinam suas técnicas com as utilizadas tradicionalmente na tarefa de reconhecimento de autoria. Esta pesquisa de mestrado propõe extensões à modelagem tradicional de co-ocorrência e investiga a adequabilidade de novos atributos e de outras modelagens (como as redes mesoscópicas e de entidades nomeadas) para a tarefa. A informação de conectividade de palavras funcionais é utilizada para complementar a caracterização da escrita dos autores, uma vez que essas palavras são relevantes para a tarefa. Finalmente, a maior contribuição deste trabalho consiste no desenvolvimento de classificadores híbridos, denominados labelled motifs, que combinam fatores tradicionais com as propriedades fornecidas pela análise topológica de redes complexas. A relevância desses classificadores é verificada no contexto de reconhecimento de autoria e identificação de translationese. Com esta abordagem híbrida, mostra-se que é possível melhorar o desempenho de técnicas baseadas em rede ao combiná-las com técnicas tradicionais em PLN. Através da adaptação, combinação e aperfeiçoamento da modelagem, não apenas o desempenho dos sistemas de reconhecimento de autoria foi melhorado, mas também foi possível entender melhor quais são os fatores quantitativos textuais (medidos via redes) que podem ser utilizados na área de estilometria. Os avanços obtidos durante este projeto podem ser utilizados para estudar aplicações relacionadas, como é o caso da análise de inconsistências estilísticas e plagiarismos, e análise da complexidade textual. Além disso, muitos dos métodos propostos neste trabalho podem ser facilmente aplicados em diversas línguas naturais.

Redes complexas têm sido cada vez mais utilizadas para a modelagem e análise dos mais diversos sistemas da natureza. Um dos tópicos mais estudados na área de redes está relacionado com a identificação e caracterização de grupos de nós mais conectados entre si do que com o restante da rede, chamados de comunidades. Neste trabalho, mostramos que comunidades podem ser caracterizadas por quatro classes gerais de propriedades, relacionadas com a topologia interna, dinâmica interna, fronteira topológica, e fronteira dinâmica das comunidades. Verificamos como estas diferentes características influenciam em dinâmicas ocorrendo sobre a rede. Em especial, estudamos o inter-relacionamento entre a topologia e a dinâmica das comunidades para cada uma dessas quatro classes de atributos. Mostramos que certas propriedades provocam a alteração desse inter-relacionamento, dando origem ao que chamamos de comportamento específico de comunidades. De forma a apresentarmos e analisarmos este conceito nos quatro casos considerados, estudamos as seguintes combinações topológicas e dinâmicas. Na primeira, investigamos o passeio aleatório tradicional ocorrendo sobre redes direcionadas, onde mostramos que a direção das conexões entre comunidades é o principal fator de alteração no relacionamento topologia-dinâmica. Aplicamos a metodologia proposta em uma rede real, definida por módulos corticais de animais do gênero Macaca. O segundo caso estudado aborda o passeio aleatório enviesado ocorrendo sobre redes não direcionadas. Mostramos que o viés associado às transições da dinâmica se tornam cada vez mais relevantes com o aumento da modularidade da rede. Verificamos também que a descrição da dinâmica a nível de comunidades possibilita modelarmos com boa acurácia o fluxo de passageiros em aeroportos. A terceira análise realizada envolve a dinâmica neuronal integra-e-dispara ocorrendo sobre comunidades geradas segundo o modelo Watts-Strogatz. Mostramos que as comunidades podem possuir não apenas diferentes níveis de ativação dinâmica, como também apresentar diferentes regularidades de sinal dependendo do parâmetro de reconexão utilizado na criação das comunidades. Por último, estudamos a influência das posições de conexões inibitórias na dinâmica integra-e-dispara, onde mostramos que a inibição entre comunidades dá origem a interessantes variações na ativação global da rede. As análises realizadas revelam a importância de, ao modelarmos sistemas reais utilizando redes complexas, considerarmos alterações de parâmetros do modelo na escala de comunidades. / There has been a growing interest in modeling diverse types of real-world systems through the tools provided by complex network theory. One of the main topics of research in this area is related to the identification and characterization of groups, or communities, of nodes more densely connected between themselves than with the rest of the network. We show that communities can be characterized by four general classes of features, associated with the internal topology, internal dynamics, topological border, and dynamical border of the communities. We verify that these characteristics have direct influence on the dynamics taking place over the network. Particularly, for each considered class we study the interdependence between the topology and the dynamics associated with each network community. We show that some of the studied properties can influence the topology-dynamics interdependence, inducing what we call the communities specific behavior. In order to present and characterize this concept on the four considered classes, we study the following combinations of network topology and dynamics. We first investigate traditional random walks taking place on a directed network. We demonstrate that, for this dynamics, the direction of the edges between communities represents the main method for the modification of the topology-dynamics relationship. We apply the developed approach on a real-world network, defined by the connectivity between cortical regions in primates of the Macaca genus. The second studied case considers the biased random walk on undirected networks. We demonstrate that the transition bias of this dynamics becomes more relevant for higher network modularity. In addition, we show that the biased random walk can be used to model with good accuracy the passenger flow inside the communities of two airport networks. The third analysis is done on a neuronal dynamics, called integrate-and-fire, applied to networks composed of communities generated by the Watts-Strogatz model. We show that the considered communities can not only posses distinct dynamical activation levels, but also yield different signal regularity. Lastly, we study the influence of the positions of inhibitory connections on the integrate-and-fire dynamics. We show that inhibitory connections placed between communities can have a non-trivial influence on the global behavior of the dynamics. The current study reveals the importance of considering parameter variations of network models at the scale of communities.