The global financial system is an intricate network of networks, and recent financial crises have laid bare our insufficient understanding of its complexity. In response, within the five chapters of this thesis we study how interconnectedness, interdependency and mutual influence impact financial markets and systemic risk. In the first part, we investigate the community formation of global equity and currency markets. We find remarkable changes to correlation structure and lead-lag relationships in times of economic turmoil, implying significant risks to diversification based on historical data. The second part focuses on banks as creators of credit. Bank portfolios generally share some overlap, and this may introduce systemic risk. We model this using European stress test data, finding that the system is stable across a broad range of asset liquidity and risk tolerance. However, there exists a phase transition: If banks become sufficiently risk averse, even small shocks may inflict great losses. Failure to address portfolio overlap thus may leave the banking system ill-prepared. Complete knowledge of the financial network is prerequisite to such systemic risk analyses. When lacking this knowledge, maximum entropy methods allow a probabilistic reconstruction. In the third part of this thesis, we consider Japanese firm-bank data and find that reconstruction methods fail to generate a connected network. Deriving an analytical expression for connection probabilities, we show that this is a general problem of sparse graphs with inhomogeneous layers. Our results yield confidence intervals for the connectivity of a reconstruction. The maximum entropy approach also proves useful for studying dependencies in financial markets: On its basis, we develop a new measure for the information content in foreign exchange rates in part four of this thesis and use it to study the impact of macroeconomic variables on the strength of currency co-movements. While macroeconomic data and the law of supply and demand drive financial markets, foreign exchange rates are also subject to policy interventions. In part five, we classify the roles of currencies within the market with a clustering algorithm and study changes after political and monetary shocks. This methodology may further provide a quantitative underpinning to existing qualitative classifications. / 2019-12-11T00:00:00Z

Submitted by Fabio Sobreira Campos da Costa (fabio.sobreira@ufpe.br) on 2016-08-08T13:00:48Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) tese_MariaSibaldo.pdf: 2571529 bytes, checksum: 0d9af192f329870166c194c53541ce82 (MD5) / Made available in DSpace on 2016-08-08T13:00:48Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) tese_MariaSibaldo.pdf: 2571529 bytes, checksum: 0d9af192f329870166c194c53541ce82 (MD5) Previous issue date: 2016-02-23 / CAPEs / Os sistemas de recomendação fazem uso de algoritmos para facilitar a busca de itens de interesse do usuário. Esta tese apresenta uma solução para recomendação através do agrupamento em redes complexas, dado que este encontra padrões que beneficiam a recomendação. É utilizada a métrica de modularidade para auxiliar na divisão de uma rede em grupos e, com base nesse agrupamento, realizar recomendação. Assim, foram propostos dois métodos de recomendação baseados em modularidade, dois algoritmos de agrupamento e uma nova métrica de modularidade. O primeiro método proposto estima o peso da aresta entre dois elementos em uma rede bipartida (usuário e item) após a formação de grupos e faz uso das arestas do grupo do item. O método citado anteriormente serviu de inspiração para o segundo método, o qual faz uso das arestas entre grupos. Para este segundo método foram propostos dois algoritmos: AMV (Agrupamento com Movimento de Vértices), o qual realiza os agrupamentos com diversas métricas existentes; e o AMA (Agrupamento com Movimento de Arestas), o qual realiza agrupamentos apenas com a métrica proposta. O algoritmo AMA tem um tempo de processamento menor que o AMV. Com as observações realizadas na segunda proposta, uma nova métrica de modularidade foi elaborada para melhorar a recomendação. Esta modularidade possui maior valor quando os pesos dos relacionamentos entre os grupos são semelhantes. A primeira proposta se mostrou adequada para o problema e obteve o 6º lugar na competição do RecSys 2014. A segunda proposta obteve resultados comparativos equivalentes ao de métodos de recomendação no estado-da-arte. A métrica proposta mostrou-se adequada para a recomendação. / This thesis uses the modularity metric to assist in dividing a network into groups and, based on this grouping, apply recommendation procedure. We propose two methods of recommendation based on modularity, two grouping algorithm and also a new metric of modularity. The first method proposed estimates the rating between two nodes in a bipartite network after grouping it, for this estimation the item’s group is used. The first method was the inspiration for the second one: which uses the edges between groups to estimate the edges weight. Two algorithms were created for this second method: AMV (grouping with vertex movement), which can be used with different modularity metrics; and AMA (grouping with edges moviment), which makes use of the modularity metric proposed here and is faster than the previous one. A different modularity metric was proposed to improve the recommendation system. This modularity has greater value when the weights of relationships between groups are similar. The first proposal was adequate to the problem and obtained the 6th place in the RecSys Challenge 2014 competition. The second proposal has equivalent results compared to other recommendations methods in the state of the art. The experiments with the proposal metric showed that this metric is adequate to recommender systems.

Real world systems like biological, social, technological, infrastructural and many others can be modeled as networks. The field of network science aims to study these complex networks and understand their structure and dynamics. A common feature of networks across domains is the distribution of the degree of the nodes according to a power-law (scale invariance). As a consequence of this skewness, the high degree nodes dominate the properties of these networks. The rich-club phenomenon is observed when the high degree or the rich nodes of the network prefer to connect amongst themselves. In the first part, the thesis investigates the rich-club phenomenon in higher order neighborhoods of the network by providing an elegant quantification using a geodesic distance based approach. This quantification helped in identifying networks where the trend and intensity of the rich-club phenomenon is significantly different in higher order neighborhoods compared to the immediate neighbors. The thesis also proposes a quantification of the importance of the non-rich nodes in the communication structure of the rich nodes, and broadly classify networks into core-periphery or cellular. Further a lack of universality is noticed in the structure of the networks belonging to a particular domain. It has been observed in the previous literature that the rich club connectivity dominates assortativity, a measure quantifying the mixing patterns in complex networks. Thus, assortativity is biased. To overcome such drawbacks, in the second part of the thesis proposes a novel measure called regularity. The analytical bounds on regularity and formulation of regularity for different network models are provided. Along with this a measure to quantify the mixing patterns of the neighborhood of a node called local regularity is also defined. The analysis on real-world network based on local regularity and degree distribution shows presence of both type of network, uniformly and non-uniformly mixed across different regions. Further normalized regularity is proposed to quantify the extent of preferential mixing in networks discounting the effect of degree distribution.

The search for a satisfactory model for complexity, meant as an intermediate condition between total order and total disorder, is still subject of debate in the scientific community. In this dissertation the emergence of non-Poisson renewal processes in several complex systems is investigated. After reviewing the basics of renewal theory, another popular approach to complexity, called modulation, is introduced. I show how these two different approaches, given a suitable choice of the parameter involved, can generate the same macroscopic outcome, namely an inverse power law distribution density of events occurrence. To solve this ambiguity, a numerical instrument, based on the theoretical analysis of the aging properties of renewal systems, is introduced. The application of this method, called renewal aging experiment, allows us to distinguish if a time series has been generated by a renewal or a modulation process. This method of analysis is then applied to several physical systems, from blinking quantum dots, to the human brain activity, to seismic fluctuations. Theoretical conclusions about the underlying nature of the considered complex systems are drawn.

Spatial and topological features of urban road networks have been observed variously in the past. No previous study, however, has investigated and compared an extensive data set from cities all over the world regarding their network properties. In this work, re-spectively 20 large cities from 5 continents and Germany are analyzed. In the process, node degree, link length, shortest paths, detour index as well as measures for rectangu-larity are used to characterize and to differentiate the networks. While most networks properties are quite diverse from continent to continent, the detour index as a measure of efficiency shows remarkable similarities and homogeneity over all regions, independ-ent of their spatial network structure. It is shown that in some cities this efficiency is mainly sustained by a subnetwork of major roads, while in others it relies on a balance between minor and major roads. Rectangularity in all regions is shown to be predomi-nant in the structure of minor road subnetworks, while it is shown that this feature is not trivially connected to the node degree. / Räumliche und topografische Eigenschaften urbaner Straßennetzwerke sind in der Ver-gangenheit vielfältig untersucht wurden. Keine der bisherigen Studien hat jedoch eine umfassende Anzahl weltweiter Städte auf ihre Netzwerkeigenschaften untersucht. In dieser Arbeit werden jeweils 20 Großstädte aus 5 Kontinenten analysiert. Knotengrad, Kantenlängen, kürzeste Pfade, Detour Index sowie die Rechtwinkligkeit werden schritt-weise untersucht, um die Netzwerke zu charakterisieren und voneinander zu differen-zieren. Während die meisten Netzwerkmaße große Unterscheide von Kontinent zu Kon-tinent aufweisen, lassen sich beim Detour Index, welcher ein Maß für die Effizienz im Netzwerk dient, bemerkenswerte Gemeinsamkeiten in allen Regionen unabhängig von der räumlichen Netzwerkstruktur feststellen. Es wird gezeigt, dass die Effizienz in eini-gen Städten hauptsächlich durch ein Teilnetz von Hauptstraßen getragen wird, während sie anderswo auf einer Balance zwischen Haupt- und Nebenstraßen beruht. Vor allem in der Struktur von Nebenstraßennetzwerken kann Rechtwinkligkeit festgestellt werden, während gleichzeitig wird, dass letztere in keinem trivialen Zusammenhang mit dem Knotengrad steht.

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.

Doctor of Philosophy / Department of Electrical and Computer Engineering / Caterina Scoglio / The science of complex networks has significantly advanced in the last decade and has provided valuable insights into the properties of real world systems by evaluating their structure and construction. Several phenomena occurring in real technological and social systems can be studied, evaluated, quantified, and remedied with the help of network science. The electric power grid is one such real technological system that can be studied through the science of complex networks. The electric grid consists of three basic sub-systems: Generation, Transmission, and Distribution. The transmission sub-system is of particular interest in this work because its mesh-like structure offers challenging problems to complex networks researchers. Cascading dynamics of power grids is one of the problems that can be studied through complex networks. The North American Electric Reliability Corporation (NERC) defines a cascading failure as the uncontrolled successive loss of system elements triggered by an incident at any location. In this dissertation, we primarily discuss the dynamics of cascading failures in the power transmission grid, from a complex networks perspective, and propose possible solutions for mitigating their effects. We evaluate the grid dynamics for two specific scenarios, load growth and random fluctuations in the grid, to study the behavior of the grid under critical conditions. Further, we propose three mitigation strategies for reducing the damage caused by cascading failures. The first strategy is intentional islanding in the power transmission grid. The aim of this method is to intentionally split the grid into two or more separate self- sustaining components such that the initial failure is isolated and the separated components can function independently, with minimum load shedding. The second mitigation strategy involves controlled placement of distributed generation (DG) in the transmission system in order to enhance robustness of the grid. The third strategy requires the addition of a link in the transmission grid by reduction of the average spectral distance, utilizing the Ybus matrix of the grid and a novel algorithm. Through this dissertation, we aim to successfully cover the gap present in the complex networks domain, with respect to the vulnerability analysis of power grid networks.

Este estudo visa, por meio de simulações computacionais, compreender de que modo a centralidade dos agentes, os diferentes tipos de rede e o não- compartilhamento da informação afetam os processos de adoção e de difusão de diferentes tipos de inovações. Para esta tarefa, foram feitas simulações com os modelos de rede descritos por Watts e Strogatz (WATTS & STROGATZ, 1998), com um número fixo de 100 nós ou agentes (n=100), em que foram variados os parâmetros de mi (centralidade de grau inicial) e p (probabilidade de reconexão desses nós). Foram utilizadas redes regulares (p=0), de pequeno-mundo (p=0.5) e aleatórias (p=1.0). Escolhemos os adotantes iniciais por 5 diferentes métodos: por maiores centralidades de grau, de proximidade, de intermediação e de Bonacich, além da escolha aleatória. Também consideramos dois tipos de agentes com diferentes características: o primeiro, chamado de social, compartilhava e recebia informação, o segundo, chamado de egoísta, recebia e não compartilhava informação. Para parte das simulações, utilizamos uma inovação contínua e, para outra parte, utilizamos uma inovação disruptiva. Como resultado, constatamos, para as simulações realizadas neste modelo, o maior crescimento do número de adotantes não dependeu somente das características da rede, mas também dos agentes que a compõem, além da própria inovação. Por esse motivo, temos que o modelo disruptivo favorece a inovação e a presença de agentes egoístas funciona como um obstáculo ou barreira / This study aims, through computational simulations, to understand how the influence of the different types of network, the centrality of agents and the non-sharing of information affect the processes of adoption and diffusion in different types of innovations. For this task, simulations were made with the network models described by Watts and Strogatz (WATTS & STROGATZ, 1998), with a fixed number of 100 nodes or agents (n = 100), in which the parameters mi (initial degree centrality) and p (probability of reconnection of these nodes) were varied. We used regular (p=0), small- world (p=0.5) and random networks (p=1.0). We choose the initial adopters by 5 different methods: by greater degree, closeness, of betweeness and eingevector centralities, besides the random choice. We also considered two types of agents with different characteristics: the first, called social, shared and received information, the second, called selfish, received and did not share information. For part of the simulations, we use a continuous innovation and, for another part, we use a disruptive innovation. As a result, for the simulations carried out in this model, the greatest growth in the number of adopters was not only dependent on the characteristics of the network, but also on the agents that compose it, besides the innovation itself. For this reason, we think that the disruptive model favors innovation and the presence of selfish agents as an obstacle or barrier

The complex network theory has been extensively used to understand various natural and artificial phenomena made of interconnected parts. This representation enables the study of dynamical processes running on complex systems, such as epidemics and rumor spreading. The evolution of these dynamical processes is influenced by the organization of the network. The size of some real world networks makes it prohibitive to analyse the whole network computationally. Thus it is necessary to represent it by a set of topological measures or to reduce its size by means of sampling. In addition, most networks are samples of a larger networks whose structure may not be captured and thus, need to be inferred from samples. In this work, we study both problems: the influence of the structure of the network in spreading processes and the effects of sampling in the structure of the network. Our results suggest that it is possible to predict the final fraction of infected individuals and the final fraction of individuals that came across a rumor by modeling them with a beta regression model and using topological measures as regressors. The most influential measure in both cases is the average search information, that quantifies the ease or difficulty to navigate through a network. We have also shown that the structure of a sampled network differs from the original network and that the type of change depends on the sampling method. Finally, we apply four sampling methods to study the behaviour of the epidemic threshold of a network when sampled with different sampling rates and found out that the breadth-first search sampling is most appropriate method to estimate the epidemic threshold among the studied ones. / Vários fenômenos naturais e artificiais compostos de partes interconectadas vem sendo estudados pela teoria de redes complexas. Tal representação permite o estudo de processos dinâmicos que ocorrem em redes complexas, tais como propagação de epidemias e rumores. A evolução destes processos é influenciada pela organização das conexões da rede. O tamanho das redes do mundo real torna a análise da rede inteira computacionalmente proibitiva. Portanto, torna-se necessário representá-la com medidas topológicas ou amostrá-la para reduzir seu tamanho. Além disso, muitas redes são amostras de redes maiores cuja estrutura é difícil de ser capturada e deve ser inferida de amostras. Neste trabalho, ambos os problemas são estudados: a influência da estrutura da rede em processos de propagação e os efeitos da amostragem na estrutura da rede. Os resultados obtidos sugerem que é possível predizer o tamanho da epidemia ou do rumor com base em um modelo de regressão beta com dispersão variável, usando medidas topológicas como regressores. A medida mais influente em ambas as dinâmicas é a informação de busca média, que quantifica a facilidade com que se navega em uma rede. Também é mostrado que a estrutura de uma rede amostrada difere da original e que o tipo de mudança depende do método de amostragem utilizado. Por fim, quatro métodos de amostragem foram aplicados para estudar o comportamento do limiar epidêmico de uma rede quando amostrada com diferentes taxas de amostragem. Os resultados sugerem que a amostragem por busca em largura é a mais adequada para estimar o limiar epidêmico entre os métodos comparados.

Cities are complex systems of transportation and social activity; their structure can be used to model urban street networks i.e. complex network that represents the geometry of a city allowing analytical activities for data-driven decision-making. The geometry of a city holds intrinsic information that can support activities related to the analysis of the urban scenario; of higher importance is the use of such information to enhance the quality of life of its inhabitants and/or to understand the dynamics of an urban center. Several of these analytical processes lacks in-depth methodologies to analyze crime patterns and ill-designed urban structures, which can provide for public safety and urban design. Consequently, it is our goal to provide means for the structural and topological analysis of highly criminal regions of cities represented as complex networks, and for the identification of urban planning inconsistencies that point to regions that lack access from/to points of interest in a city. In this regard, we devised a set of algebraic and algorithmic procedures that are capable of revealing patterns and provide for data comprehension. More specifically, we introduced pre-processing techniques to transform georeferenced electronic maps into graph representations of cities; we used metric-based and epidemic processes to understand the dynamics of cities in what refers to criminality; finally, we introduced a novel set of formalisms and operations based on set theory to identify design flaws concerning access in urban centers. Our results refer to approaches to preprocess and prepare maps in the form of urban street networks; to the analyses of crimes based on their spatial disposition; to the development of a model to describe criminal activities; and, to the advance of a concept based on critical problems in the urban design. / As cidades são sistemas complexos de interação social e de transporte. Suas estruturas podem ser usadas para modelar redes de mobilidade urbana i.e. redes complexas que representam a geometria de uma cidade permitindo a consecução de atividades analíticas para descoberta de padrões e para a tomada de decisão baseada em dados. A geometria da cidade carrega informações intrínsecas que auxiliam atividades relacionadas à análise de dados provenientes do cenário urbano. As informações inerentes a tais análises podem ser usadas para melhorar a qualidade de vida dos habitantes de uma região, ou para entender a dinâmica de centros urbanos. Diversos processos analíticos aplicados a tais cenários carecem de metodologias para analisar o padrão criminal e para identificar estruturas urbanas mal planejadas. Deste modo, este trabalho tem por objetivo prover meios para análise topológica de regiões criminais e para a identificação de inconsistências urbanas, as quais apontam para regiões que carecem de mobilidade e acesso para outras regiões de uma cidade. Neste sentido, foi desenvolvido um conjunto de procedimentos algébricos e algorítmicos capazes de revelar padrões e meios para compreensão e análise dos dados. Mais especificamente, foram desenvolvidos métodos de pré-processamento para transformar mapas eletrônicos georreferenciados em grafos que representam cidades, foi utilizado um conjunto métrico analítico e outro com base em processos epidêmicos para entender a dinâmica intrínseca à criminalidade de uma cidade, e por fim, foi desenvolvido um conjunto de formalismos e operações baseados em teoria dos conjuntos para identificar falhas no desenho das estruturas urbanas que impactam no acesso viário em centros urbanos. Os resultados deste trabalho versam sobre o desenvolvimento de novos métodos para preparar mapas na forma de redes de mobilidade urbana; na análise de crimes baseada em sua disposição espacial; no desenvolvimento de um modelo capaz de descrever a atividade criminal de uma cidade; e, em um conceito baseado na análise de regiões críticas identificadas a partir do desenho urbano.