A inferência de redes gênicas (GN) a partir de dados de expressão gênica temporal é um problema crucial e desafiador em Biologia Sistêmica. Os conjuntos de dados de expressão geralmente consistem em dezenas de amostras temporais e as redes consistem em milhares de genes, tornando inúmeros métodos de inferência inviáveis na prática. Para melhorar a escalabilidade dos métodos de inferência de GNs, esta tese propõe um arcabouço chamado GeNICE, baseado no modelo de redes gênicas probabilísticas. A principal novidade é a introdução de um procedimento de agrupamento de genes, com perfis de expressão relacionados, para fornecer uma solução aproximada com complexidade computacional reduzida. Os agrupamentos definidos são usados para reduzir a dimensionalidade permitindo uma busca exaustiva mais eficiente pelos melhores subconjuntos de genes preditores para cada gene alvo de acordo com funções critério multivariadas. GeNICE reduz consideravelmente o espaço de busca porque os candidatos a preditores ficam restritos a um gene representante por agrupamento. No final, uma análise multivariada é realizada para cada subconjunto preditor definido, visando recuperar subconjuntos mínimos para simplificar a rede gênica inferida. Em experimentos com conjuntos de dados sintéticos, GeNICE obteve uma redução substancial de tempo quando comparado a uma solução anterior sem a etapa de agrupamento, preservando a precisão da predição de expressão gênica mesmo quando o número de agrupamentos é pequeno (cerca de cinquenta) e o número de genes é grande (ordem de milhares). Para um conjunto de dados reais de microarrays de Plasmodium falciparum, a precisão da predição alcançada pelo GeNICE foi de aproximadamente 97% em média. As redes inferidas para os genes alvos da glicólise e do apicoplasto refletem propriedades topológicas de redes complexas do tipo \"mundo pequeno\" e \"livre de escala\", para os quais grande parte das conexões são estabelecidas entre os genes de um mesmo módulo e algumas poucas conexões fazem o papel de estabelecer uma ponte entre os módulos (redes mundo pequeno), e o grau de distribuição das conexões entre os genes segue uma lei de potência, na qual a maioria dos genes têm poucas conexões e poucos genes (hubs) apresentam um elevado número de conexões (redes livres de escala), como esperado. / Gene network (GN) inference from temporal gene expression data is a crucial and challenging problem in Systems Biology. Expression datasets usually consist of dozens of temporal samples, while networks consist of thousands of genes, thus rendering many inference methods unfeasible in practice. To improve the scalability of GN inference methods, this work proposes a framework called GeNICE, based on Probabilistic Gene Networks; the main novelty is the introduction of a clustering procedure to group genes with related expression profiles, to provide an approximate solution with reduced computational complexity. The defined clusters were used to perform an exhaustive search to retrieve the best predictor gene subsets for each target gene, according to multivariate criterion functions. GeNICE greatly reduces the search space because predictor candidates are restricted to one representative gene per cluster. Finally, a multivariate analysis is performed for each defined predictor subset to retrieve minimal subsets and to simplify the network. In experiments with in silico generated datasets, GeNICE achieved substantial computational time reduction when compared to an existing solution without the clustering step, while preserving the gene expression prediction accuracy even when the number of clusters is small (about fifty) relative to the number of genes (order of thousands). For a Plasmodium falciparum microarray dataset, the prediction accuracy achieved by GeNICE was roughly 97% on average. The inferred networks for the apicoplast and glycolytic target genes reflects the topological properties of \"small-world\"and \"scale-free\"complex network models in which a large part of the connections is established between genes of the same functional module (smallworld networks) and the degree distribution of the connections between genes tends to form a power law, in which most genes present few connections and few genes (hubs) present a large number of connections (scale-free networks), as expected.

Diversos pesquisadores têm se utilizado do recurso de Redes de Interação na área de Biodiversidade para analisar o papel das espécies na estrutura da uma rede cujos fundamentos conceituais são os mesmos das Redes Sociais (como Facebook, LinkedIn, entre outras). Nesse sentido, algoritmos, métricas e recursos computacionais e estatísticos provenientes da área de Análise de Redes Sociais (Social Network Analysis SNA) são ferramentas importantes para endereçar/apoiar estudos com interações. Assim sendo, o objetivo desta tese é propor uma metodologia para aplicação das métricas de SNA em estudos com Redes de Interação biológicas no domínio da Informática para a Biodiversidade. A metodologia está formalizada por meio da Notação para Modelagem de Processos de Negócio (BPMN - Business Process Model and Notation) e estruturada em quatro etapas: (i) mapeamento dos tipos de dados e de interação disponíveis; (ii) definição das perguntas-chave a serem respondidas e das variáveis de análise; (iii) escolha das métricas de SNA adequadas ao contexto da pesquisa; e (iv) realização de análises biológicas com o apoio de SNA. Como recursos materiais foram utilizadas as métricas de SNA, bem como um conjunto de ferramentas computacionais (como os pacotes do R e os programas Dieta, Pajek e Ucinet) e de Análise Estatística (como a Análise Exploratória de Dados e a Análise Multivariada de Dados). Esta proposta nasceu de um processo de colaboração com pesquisadores de diversas áreas do conhecimento, a partir de projetos desenvolvidos no Núcleo de Pesquisa em Biodiversidade e Computação da USP (BioComp-USP), o que trouxe uma base de sustentação a esta metodologia. Para avaliar a adequação desta proposta a necessidades reais de pesquisa a metodologia foi aplicada a três estudos de caso com Redes de Interação microbiológicas. Os resultados mostram os benefícios que a disponibilização de um método sistematizado para guiar os passos de uma pesquisa pode trazer a um pesquisador seja em função do aporte de recursos recomendados, seja pelo processo de organização das atividades de pesquisa. Além disso, verifica-se a possibilidade de transposição desta proposta a outros domínios do conhecimento ainda não explorados, como em Agrobiodiversidade. / Several researchers have used Interaction Networks resources in the Biodiversity area for analyzing the role of species in network structure their conceptual foundations are the same as those in Social Networks (such as Facebook, LinkedIn, among others). Thus, algorithms, metrics, and statistical and computational resources from the Social Network Analysis (SNA) area are important tools for addressing this issue. Therefore, the aim of this thesis is to propose a methodology for applying SNA metrics to biological interaction network studies in the Biodiversity Informatics domain. The methodology is formalized by means of Business Process Model and Notation (BPMN) and structured in four steps: (i) mapping the data types and the interaction available; (ii) defining the key-questions to be answered and the analysis variable; (iii) choosing the SNA metrics appropriate to the context of the research; and (iv) performing the biological analysis with the support of SNA. As material resources, the SNA metrics were used, as well as a set of computational (such as R packages, Dieta, Pajek and Ucinet software) and Statistical Analysis (Exploratory Data Analysis and Multivariate Data Analysis) tools. This proposal generated a process collaboration with researchers from different knowledge areas, by means of projects developed at the Research Center on Biodiversity and Computing at USP (BioComp-USP), which provided a support base to this methodology. To assess the suitability of this proposal to the real research needs, it was applied to three case studies with microbiological Interaction Networks. The results show the benefits that providing a systematic method to guide the steps of one research can bring to a researcher be it due to the support of the resources recommended, be it by the organization of the research activities. In addition, there is the possibility of applying this methodology to unexplored knowledge fields, such as Agrobiodiversity.

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

Made available in DSpace on 2016-04-29T14:23:35Z (GMT). No. of bitstreams: 1 Patricia Kunst Canetti.pdf: 6615349 bytes, checksum: 2e3fdc72fd907b8fdac9ace8c05bdb03 (MD5) Previous issue date: 2015-04-07 / This work is a survey of Canal Contemporâneo's fourteen years of memory - www.canalcontemporaneo.art.br - and analyzes this memory and its adjacent concepts to point out the prospects of this experiment / research , which reached a surprising longevity in Brazilian cultural Internet. The rescue of its history and collective memory was done in three chapters which thread runs through the editorial sections, platforms and actions of Canal Contemporâneo. In the first chapter we discuss its origin, the first stimuli, concepts and developments. Since then gathered actions that operate in the field of art, politics and communication, pointing to a perspective of narrative and rereading of contemporary art, with a work on Social Netwok Analysis and Data Visualization. The theoretical basis of this research that only begins is based on the following fields and authors: Data Visualization (Fernanda Viégas, Lev Manovich e Manuel Lima); Taxonomy (Marcia Lei Zeng e Jian Qin); Social Netwok Analysis (Katherine Faust e Stanley Wasserman) and models of Random Graphs (Paul Erdős e Alfréd Rényi), Small-World (Duncan J. Watts e Steven Strogatz), Preferential Attachment (Albert-László Barabási e Réka Albert); History and Sociology of Art (Aby Warburg, Alfred Gell e Bruno Latour). We hope that the new shared experience through this work can contribute to a broader view of collection, archiving and cultural heritage, for public policy of culture in Brazil / Este trabalho faz um levantamento da memória de quatorze anos de existência do Canal Contemporâneo www.canalcontemporaneo.art.br e analisa esta memória e seus conceitos adjacentes para apontar as perspectivas deste experimento/pesquisa, que atingiu uma longevidade surpreendente na Internet cultural brasileira. O resgate de sua história e memória coletiva foi feito em três capítulos cujo fio condutor perpassa as seções editoriais, as plataformas e as ações do Canal Contemporâneo. No primeiro capítulo abordamos a sua origem, os primeiros estímulos, conceitos e desdobramentos. Desde então reuniu ações que operam no campo da arte, da política e da comunicação, que apontam para uma perspectiva de narrativa e releitura da arte contemporânea, com um trabalho de Análise de Redes Sociais e Visualização de Dados. O embasamento teórico desta pesquisa que apenas se inicia se firma nos seguintes campos e autores: Visualizações de Dados (Fernanda Viégas, Lev Manovich e Manuel Lima); Taxonomia (Marcia Lei Zeng e Jian Qin); Análise de Redes Sociais (Katherine Faust e Stanley Wasserman) e dos modelos de Grafos Aleatórios (Paul Erdős e Alfréd Rényi), Small-World (Duncan J. Watts e Steven Strogatz), Preferential Attachment (Albert-László Barabási e Réka Albert); História e Sociologia da Arte (Aby Warburg, Alfred Gell e Bruno Latour). Esperamos que a nova experiência compartilhada através deste trabalho possa contribuir para uma visão mais ampla de acervo, arquivo e patrimônio cultural, para as políticas públicas de cultura no Brasil

Mathematical modeling of spreading processes have been largely studied in the literature, and its presented a boom in the past few years. This is a fundamental task on the understanding and prediction of real spreading processes on top of a population and are subject to many structural and dynamical constraints. Aiming at a better understanding of this processes, we focused in two task: the modeling and the analysis of both dynamical and structural aspects of these processes. Initially, we proposed a new and general model that unifies epidemic and rumor spreading. Besides, regarding the analysis of these processes, we extended the classical formalism to multilayer networks, in which the theory was lacking. Interestingly, this study opened up new challenges concerning the understanding of multilayer networks. More specifically, regarding their spectral properties. In this thesis, we analyzed such processes on top of single and multilayer networks. Thus, throughout our analysis, we followed three complementary approaches: (i) analytical, (ii) numerical and (iii) simulations, mainly Monte Carlo simulations. Our main results are: (i) a new unifying model, enabling us to model and understand spreading processes on large systems, (ii) characterization of new phenomena on multilayer networks, such as layer-wise localization and the barrier effect and (iii) an spectral analysis of multilayer systems, suggesting a universal parameter and proposing a new analytical tool for its analysis. Our contributions enable further research on modeling of spreading processes, also emphasizing the importance of considering the complete multilayer structure instead of any coarse-graining. Additionally, it can be directly applied on the prediction and modeling real processes. Thus, aside from the theoretical interest and its mathematical implications, it also presents important social impact. / A modelagem matemática dos processos de disseminação tem sido amplamente estudada na literatura, sendo que o seu estudo apresentou um boom nos últimos anos. Esta é uma tarefa fundamental na compreensão e previsão de epidemias reais e propagação de rumores numa população, ademais, estas estão sujeitas a muitas restrições estruturais e dinâmicas. Com o objetivo de entender melhor esses processos, nos concentramos em duas tarefas: a de modelagem e a de análise de aspectos dinâmicos e estruturais. No primeiro, propomos um modelo novo e geral que une a epidemia e propagação de rumores. Também, no que diz respeito à análise desses processos, estendemos o formalismo clássico às redes multicamadas, onde tal teoria era inexistente. Curiosamente, este estudo abriu novos desafios relacionados à compreensão de redes multicamadas, mais especificamente em relação às suas propriedades espectrais. Nessa tese, analisamos esses processos em redes de uma e múltiplas camadas. Ao longo de nossas análises seguimos três abordagens complementares: (i) análises analíticas, (ii) experimentos numéricos e (iii) simulações de Monte Carlo. Assim, nossos principais resultados são: (i) um novo modelo que unifica as dinâmicas de rumor e epidemias, nos permitindo modelar e entender tais processos em grandes sistemas, (ii) caracterização de novos fenômenos em redes multicamadas, como a localização em camadas e o efeito barreira e (iii) uma análise espectral de sistemas multicamadas, sugerindo um parâmetro de escala universal e propondo uma nova ferramenta analítica para sua análise. Nossas contribuições permitem que novas pesquisas sobre modelagem de processos de propagação, enfatizando também a importância de se considerar a estrutura multicamada. Dessa forma, as nossas contribuições podem ser diretamente aplicadas à predição e modelagem de processos reais. Além do interesse teórico e matemático, nosso trabalho também apresenta implicações sociais importantes.

<p>Almost any large-scale system can be viewed as a network that shows interac-tions among entities which are constituent parts of the system. The focus of this<br />dissertation is on complex networks from three domains: (1) networks extracted<br />from source code of computer programs that represent design of software systems,<br />(2) networks extracted from semantic web ontologies that describe the structure<br />of shared and reusable knowledge, and (3) networks extracted from bibliographic<br />records that depict collaboration in science. We proposed new methods for the<br />extraction of networks from mentioned domains. Secondly, on several case stud-ies we demonstrated benets of network-based analysis of concrete systems from<br />those domains. In contrast to the previous work on the subject, analyses pre-sented in this dissertation are not purely topological, but combine techniques and<br />metrics developed under the framework of complex network theory with domain-dependent metrics.</p> / <p>Skoro svaki kompleksan sistem se može predstaviti mrežom koja opisuje interakcije izmedju entiteta od kojih je sistem komponovan. Fokus ove disertacije je na&nbsp;kompleksnim mrežama iz tri domena: (1) mreže ekstrahovane iz izvornog koda&nbsp;računarskih programa koje reprezentuju dizajn softverskih sistema, (2) mreže ekstrahovane iz ontologija semantičkog web-a koje opisuju strukturu deljenog znanja&nbsp;pogodnog za vi&scaron;ekratnu upotrebu, i (3) mreže ekstrahovane iz bibliografskih zapisa koje opisuju saradnju istraživača. U okviru disertacije predložene su nove&nbsp;metode za ekstrakciju mreža iz pomenutih domena. Drugo, na nekoliko studija&nbsp;slučaja ilustrovani su beneti mrežno orjentisane analize konkretnih sistema iz&nbsp;domena obuhvaćenih disertacijom. U poredjenju sa prethodnim relevantim istraživanjima, analize prezentovane u disertaciji nisu čisto topolo&scaron;ke, nego kombinuju tehnike i metrike razvijene u okviru teorije kompleksnih mreža sa metrikama iz konkretnog domena.</p>

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

Les travaux de cette thèse présentent le développement d'algorithmes de classification de documents d'une part, ou d'analyse de réseaux complexes d'autre part, en s'appuyant sur la prétopologie, une théorie qui modélise le concept de proximité. Le premier travail développe un cadre pour la classification de documents en combinant une approche de topicmodeling et la prétopologie. Notre contribution propose d'utiliser des distributions de sujets extraites à partir d'un traitement topic-modeling comme entrées pour des méthodes de classification. Dans cette approche, nous avons étudié deux aspects : déterminer une distance adaptée entre documents en étudiant la pertinence des mesures probabilistes et des mesures vectorielles, et effet réaliser des regroupements selon plusieurs critères en utilisant une pseudo-distance définie à partir de la prétopologie. Le deuxième travail introduit un cadre général de modélisation des Réseaux Complexes en développant une reformulation de la prétopologie stochastique, il propose également un modèle prétopologique de cascade d'informations comme modèle général de diffusion. De plus, nous avons proposé un modèle agent, Textual-ABM, pour analyser des réseaux complexes dynamiques associés à des informations textuelles en utilisant un modèle auteur-sujet et nous avons introduit le Textual-Homo-IC, un modèle de cascade indépendant de la ressemblance, dans lequel l'homophilie est fondée sur du contenu textuel obtenu par un topic-model. / The work of this thesis presents the development of algorithms for document classification on the one hand, or complex network analysis on the other hand, based on pretopology, a theory that models the concept of proximity. The first work develops a framework for document clustering by combining Topic Modeling and Pretopology. Our contribution proposes using topic distributions extracted from topic modeling treatment as input for classification methods. In this approach, we investigated two aspects: determine an appropriate distance between documents by studying the relevance of Probabilistic-Based and Vector-Based Measurements and effect groupings according to several criteria using a pseudo-distance defined from pretopology. The second work introduces a general framework for modeling Complex Networks by developing a reformulation of stochastic pretopology and proposes Pretopology Cascade Model as a general model for information diffusion. In addition, we proposed an agent-based model, Textual-ABM, to analyze complex dynamic networks associated with textual information using author-topic model and introduced Textual-Homo-IC, an independent cascade model of the resemblance, in which homophily is measured based on textual content obtained by utilizing Topic Modeling.

To identify extreme changes in the dynamics of the Indian Summer Monsoon (ISM) in the past, I propose a new approach based on the quantification of fluctuations of a nonlinear similarity measure, to identify regimes of distinct dynamical complexity in short time series. I provide an analytical derivation for the relationship of the new measure with the dynamical invariants such as dimension and Lyapunov exponents of the underlying system. A statistical test is also developed to estimate the significance of the identified transitions. Our method is justified by uncovering bifurcation structures in several paradigmatic models, providing more complex transitions compared with traditional Lyapunov exponents. In a real world situation, we apply the method to identify millennial-scale dynamical transitions in Pleistocene proxy records of the south Asian summer monsoon system. We infer that many of these transitions are induced by the external forcing of solar insolation and are also affected by internal forcing on Monsoonal dynamics, i.e., the glaciation cycles of the Northern Hemisphere and the onset of the tropical Walker circulation. Although this new method has general applicability, it is particularly useful in analysing short palaeo-climate records. Rainfall during the ISM over the Indian subcontinent occurs in form of enormously complex spatiotemporal patterns due to the underlying dynamics of atmospheric circulation and varying topography. I present a detailed analysis of summer monsoon rainfall over the Indian peninsular using Event Synchronization (ES), a measure of nonlinear correlation for point processes such as rainfall. First, using hierarchical clustering I identify principle regions where the dynamics of monsoonal rainfall is more coherent or homogenous. I also provide a method to reconstruct the time delay patterns of rain events. Moreover, further analysis is carried out employing the tools of complex network theory. This study provides valuable insights into the spatial organization, scales, and structure of the 90th and 94th percentile rainfall events during the ISM (June to September). I furthermore analyse the influence of different critical synoptic atmospheric systems and the impact of the steep Himalayan topography on rainfall patterns. The presented method not only helps in visualising the structure of the extremeevent rainfall fields, but also identifies the water vapor pathways and decadal-scale moisture sinks over the region. Furthermore a simple scheme based on complex networks is presented to decipher the spatial intricacies and temporal evolution of monsoonal rainfall patterns over the last six decades. Some supplementary results on the evolution of monsoonal rainfall extremes over the last sixty years are also presented. / Um Extremereignisse in der Dynamik des indischen Sommermonsuns (ISM) in der geologischen Vergangenheit zu identifizieren, schlage ich einen neuartigen Ansatz basierend auf der Quantifikation von Fluktuationen in einem nichtlinearen Ähnlichkeitsmaß vor. Dieser reagiert empfindlich auf Zeitabschnitte mit deutlichen Veränderungen in der dynamischen Komplexität kurzer Zeitreihen. Ein mathematischer Zusammenhang zwischen dem neuen Maß und dynamischen Invarianten des zugrundeliegenden Systems wie fraktalen Dimensionen und Lyapunovexponenten wird analytisch hergeleitet. Weiterhin entwickle ich einen statistischen Test zur Schätzung der Signifikanz der so identifizierten dynamischen Übergänge. Die Stärken der Methode werden durch die Aufdeckung von Bifurkationsstrukturen in paradigmatischen Modellsystemen nachgewiesen, wobei im Vergleich zu den traditionellen Lyapunovexponenten eine Identifikation komplexerer dynamischer Übergänge möglich ist. Wir wenden die neu entwickelte Methode zur Analyse realer Messdaten an, um ausgeprägte dynamische Veränderungen auf Zeitskalen von Jahrtausenden in Klimaproxydaten des südasiatischen Sommermonsunsystems während des Pleistozäns aufzuspüren. Dabei zeigt sich, dass viele dieser Übergänge durch den externen Einfluss der veränderlichen Sonneneinstrahlung, sowie durch dem Klimasystem interne Einflussfaktoren auf das Monsunsystem (Eiszeitzyklen der nördlichen Hemisphäre und Einsatz der tropischenWalkerzirkulation) induziert werden. Trotz seiner Anwendbarkeit auf allgemeine Zeitreihen ist der diskutierte Ansatz besonders zur Untersuchung von kurzen Paläoklimazeitreihen geeignet. Die während des ISM über dem indischen Subkontinent fallenden Niederschläge treten, bedingt durch die zugrundeliegende Dynamik der atmosphärischen Zirkulation und topographische Einflüsse, in äußerst komplexen, raumzeitlichen Mustern auf. Ich stelle eine detaillierte Analyse der Sommermonsunniederschläge über der indischen Halbinsel vor, die auf Ereignissynchronisation (ES) beruht, einem Maß für die nichtlineare Korrelation von Punktprozessen wie Niederschlagsereignissen. Mit hierarchischen Clusteringalgorithmen identifiziere ich zunächst Regionen mit besonders kohärenten oder homogenen Monsunniederschlägen. Dabei können auch die Zeitverzögerungsmuster von Regenereignissen rekonstruiert werden. Darüber hinaus führe ich weitere Analysen auf Basis der Theorie komplexer Netzwerke durch. Diese Studien ermöglichen wertvolle Einsichten in räumliche Organisation, Skalen und Strukturen von starken Niederschlagsereignissen oberhalb der 90% und 94% Perzentilen während des ISM (Juni bis September). Weiterhin untersuche ich den Einfluss von verschiedenen, kritischen synoptischen Systemen der Atmosphäre sowie der steilen Topographie des Himalayas auf diese Niederschlagsmuster. Die vorgestellte Methode ist nicht nur geeignet, die Struktur extremer Niederschlagsereignisse zu visualisieren, sondern kann darüber hinaus über der Region atmosphärische Transportwege von Wasserdampf und Feuchtigkeitssenken auf dekadischen Skalen identifizieren.Weiterhin wird ein einfaches, auf komplexen Netzwerken basierendes Verfahren zur Entschlüsselung der räumlichen Feinstruktur und Zeitentwicklung von Monsunniederschlagsextremen während der vergangenen 60 Jahre vorgestellt.

Uno de los aspectos más fascinantes del mundo que nos rodea es la gran variedad de escalas a las que tienen lugar los diversos fenómenos. En muchos casos esta diversidad pone de manifiesto la estructura fractal de la Naturaleza y podemos hablar entonces de fenómenos complejos, en los que eventos de diferentes magnitudes no pueden analizarse de manera independiente. Dicha complejidad emerge como un fenómeno cooperativo a escalas microscópicas, que produce un complejo comportamiento macroscópico caracterizado por correlaciones de largo alcance e invarianza de escala. Aparecen así conceptos como leyes de escalado, universalidad y renormalización, pilares fundamentales dentro de la Física Estadística.El abanico de fenómenos complejos es muy amplio, y abarca sistemas de muy diversas disciplinas que van desde la Físicamás ortodoxa hasta la Biología, Sociología, Geología e, incluso, Economía. Esta Tesis se centra en fenómenos complejos extendidos en el espacio. En concreto hemos focalizado nuestra labor en tres grandes temas que constituyen importantes focos de interés dentro de la Mecánica Estadística: Crecimiento de Interfases, Sociofísica y Redes Complejas. / The ubiquity of complexity in Nature provides examples of a huge variety of systems to be analyzed by means of Statistical Mechanics and leads to the interconnection among various scientific disciplines. This Thesis focuses on three highlight topics of spatially extended complex systems: Interface Growth,Sociophysics, and Complex Networks. The document has been partitioned in three separated parts according to those topics.The first part deals with far-from-equilibrium growing interfaces. This subject represents one of the main fields in which fractal geometry has been widely applied, and is nowadays of great interest in Condensed Matter Physics. The Chapter 2 provides a brief and basic introduction to interface growth. We introduce some fractal and scaling concepts, as well as the main universality classes in presence of annealed disorder (EW and KPZ) in terms of both growth equations and discrete models. In Chapter 3 we focus on the elastic interface dynamics in disordered media, i.e., in presence of quenched randomness. This Chapter contains original research based on cellular automata simulations. We carry out a novel study of the dynamics by focusing on the discrete activity patterns that the interface sites describe during therelaxation toward the steady state. We analyze the spatio-temporal correlations of such patterns as the temperature is varied. We observe that, for some range of low temperatures, the out-of-equilibrium relaxation can be understood in the context of creep dynamics.The second part of the Thesis focus on Sociophysics. This discipline attends to the social interactions among individuals -most often mapped onto networks to provide them a topological structure- and has recently attracted much interest in the physics community. Social interactions give rise to adaptive systems that exhibit complex features as self-organization and cooperation. Therefore, Statistical Mechanics provides the necessary tools to analyze the behavior of such groups of agentsin a first level of simplification. The topics that Sociophysics deals with are quite a number, and we particularly focus on processes of opinion formation. The Chapter 4 presents a basic classification of the different opinion formation models present in the literature. In Chapter 5 we provide some analytical and numerical own results to describe the effect that the social temperature- understood as a simplified description of the interplay between an agent, its surroundings, and a collective climate parameter- may exert on such opinion formation processes. The thermal effect can be implemented in different ways. In the first part of the Chapter we work on a simple opinion formation model that, according to some procedural rules, reproduces the Sznajd dynamics. We include the thermal effect by means of some probability that the agents adopt the opposite opinion that the one indicated by such rules. In the second part of the Chapterwe consider a system with three different interacting groups of individuals, where the thermal effect is implemented as certain probability of spontaneous changes of the agents opinion. We exploit the van Kampen's expansion approach to analyze the macroscopic behavior of the different supporter group densities as well as the fluctuations around such macroscopic behavior.The third and last part of the document concerns Complex Networks, which have recently prompted the scientific community to investigate the mechanisms that determine their topology and dynamical properties.The rapid development of networks like the Internet and the World-Wide-Web, which represent today the basic substrate for all sort of communications at planetary level, has given rise to a number of interdisciplinary studies with highly technological applications. We first provide an introduction to complex networks in Chapter 6, where we introduce some basic concepts as scale-free graphs, mixing patterns, clustering coefficient, and small-world effect. In Chapter 7 we deal with traffic processes on networks, and specifically we focus on optimization of the routing protocols that define the connecting paths among all the pair of nodes. Such optimization pursues to avoid the traffic jams that emerge for huge quantities of matter or information flowing inthe graph. We propose an optimization algorithm that, in order to avert jamming, minimizes the number of paths that go through the most visited node (maximal betweenness) while keeping the path length as short as possible, i.e., in the proximities of the length distribution of the initial shortest-path protocol.