Computational modeling is of fundamental significance in mapping possible disease spread, and designing strategies for its mitigation. Conventional contact networks implement the simulation of interactions as random occurrences, presenting public health bodies with a difficult trade off between a realistic model granularity and robust design of intervention strategies. Recently, researchers have been investigating the use of agent-based models (ABMs) to embrace the complexity of real world interactions. At the same time, theoretical approaches provide epidemiologists with general optimization models in which demographics are intrinsically simplified. The emerging study of affiliation networks and co-affiliation networks provide an alternative to such trade off. Co-affiliation networks maintain the realism innate to ABMs while reducing the complexity of contact networks into distinctively smaller k-partite graphs, were each partition represent a dimension of the social model. This dissertation studies the optimization of intervention strategies for infectious diseases, mainly distributed in school systems. First, concepts of synthetic populations and affiliation networks are extended to propose a modified algorithm for the synthetic reconstruction of populations. Second, the definition of multi-coaffiliation networks is presented as the main social model in which risk is quantified and evaluated, thereby obtaining vulnerability indications for each school in the system. Finally, maximization of the mitigation coverage and minimization of the overall cost of intervention strategies are proposed and compared, based on centrality measures.

Recentemente, as medidas de centralidade ganharam relevância nas pesquisas com redes complexas e redes sociais, atuando como preditores comportamentais, na identificação de elementos de poder e influência, na detecção de pontos estratégicos para a comunicação e para a transmissão de doenças. Novas métricas foram criadas e outras reformuladas, mas pouco tem sido feito para que se entenda a relação existente entre as diferentes medidas de centralidades, assim como sua relação com outras propriedades estruturais das redes em que elas são frequentemente aplicadas. Nossa pesquisa visa analisar e estudar essas relações para que sirvam de guia na aplicação das medidas de centralidade existentes em novos contextos e aplicações. Nós apresentamos também evidencias que indicam um desempenho superior das medidas conhecidas como Walk Betweenness, Information, Eigenvector and Betweenness na distinção de vértices das redes somente pelas suas características estruturais. Ainda, nós propiciamos detalhes sobre o desempenho distinto de cada métrica de acordo com o tipo de rede em que se trabalha. Adicionalmente, mostramos que várias das medidas de centralidade apresentam um alto nível de redundância e concordância entre si (com correlação superior a 0,8). Um forte indício que o uso simultâneo de várias métricas é improdutivo ou pouco eficaz. Os resultados da nossa pesquisa reforçam a ideia de que para usar apropriadamente as medidades de centralidade é de extrema importância que se saiba mais sobre o comportamento e propriedades das mesmas, fato que salientamos nessa dissertação. / Over the last years, centrality measures have gained importance within complex and social networks research, e.g., as predictors of behavior, identification of powerful and influential elements, detection of critical spots in communication networks and in transmission of diseases. New measures have been created and old ones reinvented, but few have been proposed to understand the relation among measures as well as between measures and other structural properties of the networks. Our research analyzes and studies these relations with the objective of providing a guide to the application of existing centrality measures for new environments and new purposes. We shall also present evidence that the measures known as Walk Betweenness, Information, Eigenvector and Betweenness are substantially better than other metrics in distinguishing vertices in a network by their structural properties. Furthermore, we provide evidence that each metric performs better with respect to distinct kinds of networks. In addition, we show that most metrics present a high level of redundancy (over 0.8 correlation) and its simultaneous use, in most cases, is fruitless. The results achieved in our research reinforce the idea that to use centrality measures properly, knowledge about their underlying properties and behavior is valuable, as we show in this dissertation.

Recentemente, as medidas de centralidade ganharam relevância nas pesquisas com redes complexas e redes sociais, atuando como preditores comportamentais, na identificação de elementos de poder e influência, na detecção de pontos estratégicos para a comunicação e para a transmissão de doenças. Novas métricas foram criadas e outras reformuladas, mas pouco tem sido feito para que se entenda a relação existente entre as diferentes medidas de centralidades, assim como sua relação com outras propriedades estruturais das redes em que elas são frequentemente aplicadas. Nossa pesquisa visa analisar e estudar essas relações para que sirvam de guia na aplicação das medidas de centralidade existentes em novos contextos e aplicações. Nós apresentamos também evidencias que indicam um desempenho superior das medidas conhecidas como Walk Betweenness, Information, Eigenvector and Betweenness na distinção de vértices das redes somente pelas suas características estruturais. Ainda, nós propiciamos detalhes sobre o desempenho distinto de cada métrica de acordo com o tipo de rede em que se trabalha. Adicionalmente, mostramos que várias das medidas de centralidade apresentam um alto nível de redundância e concordância entre si (com correlação superior a 0,8). Um forte indício que o uso simultâneo de várias métricas é improdutivo ou pouco eficaz. Os resultados da nossa pesquisa reforçam a ideia de que para usar apropriadamente as medidades de centralidade é de extrema importância que se saiba mais sobre o comportamento e propriedades das mesmas, fato que salientamos nessa dissertação. / Over the last years, centrality measures have gained importance within complex and social networks research, e.g., as predictors of behavior, identification of powerful and influential elements, detection of critical spots in communication networks and in transmission of diseases. New measures have been created and old ones reinvented, but few have been proposed to understand the relation among measures as well as between measures and other structural properties of the networks. Our research analyzes and studies these relations with the objective of providing a guide to the application of existing centrality measures for new environments and new purposes. We shall also present evidence that the measures known as Walk Betweenness, Information, Eigenvector and Betweenness are substantially better than other metrics in distinguishing vertices in a network by their structural properties. Furthermore, we provide evidence that each metric performs better with respect to distinct kinds of networks. In addition, we show that most metrics present a high level of redundancy (over 0.8 correlation) and its simultaneous use, in most cases, is fruitless. The results achieved in our research reinforce the idea that to use centrality measures properly, knowledge about their underlying properties and behavior is valuable, as we show in this dissertation.

Recentemente, as medidas de centralidade ganharam relevância nas pesquisas com redes complexas e redes sociais, atuando como preditores comportamentais, na identificação de elementos de poder e influência, na detecção de pontos estratégicos para a comunicação e para a transmissão de doenças. Novas métricas foram criadas e outras reformuladas, mas pouco tem sido feito para que se entenda a relação existente entre as diferentes medidas de centralidades, assim como sua relação com outras propriedades estruturais das redes em que elas são frequentemente aplicadas. Nossa pesquisa visa analisar e estudar essas relações para que sirvam de guia na aplicação das medidas de centralidade existentes em novos contextos e aplicações. Nós apresentamos também evidencias que indicam um desempenho superior das medidas conhecidas como Walk Betweenness, Information, Eigenvector and Betweenness na distinção de vértices das redes somente pelas suas características estruturais. Ainda, nós propiciamos detalhes sobre o desempenho distinto de cada métrica de acordo com o tipo de rede em que se trabalha. Adicionalmente, mostramos que várias das medidas de centralidade apresentam um alto nível de redundância e concordância entre si (com correlação superior a 0,8). Um forte indício que o uso simultâneo de várias métricas é improdutivo ou pouco eficaz. Os resultados da nossa pesquisa reforçam a ideia de que para usar apropriadamente as medidades de centralidade é de extrema importância que se saiba mais sobre o comportamento e propriedades das mesmas, fato que salientamos nessa dissertação. / Over the last years, centrality measures have gained importance within complex and social networks research, e.g., as predictors of behavior, identification of powerful and influential elements, detection of critical spots in communication networks and in transmission of diseases. New measures have been created and old ones reinvented, but few have been proposed to understand the relation among measures as well as between measures and other structural properties of the networks. Our research analyzes and studies these relations with the objective of providing a guide to the application of existing centrality measures for new environments and new purposes. We shall also present evidence that the measures known as Walk Betweenness, Information, Eigenvector and Betweenness are substantially better than other metrics in distinguishing vertices in a network by their structural properties. Furthermore, we provide evidence that each metric performs better with respect to distinct kinds of networks. In addition, we show that most metrics present a high level of redundancy (over 0.8 correlation) and its simultaneous use, in most cases, is fruitless. The results achieved in our research reinforce the idea that to use centrality measures properly, knowledge about their underlying properties and behavior is valuable, as we show in this dissertation.

Study of complex networks by researchers from many disciplines has provided penetrating insights on various complex systems. A study of the world wide web from a network theoretic perspective has led to the design of new search engines [65]. The spread of diseases is now better understood by analyzing the underlying social network [26]. The study of metabolic networks, protein-protein interaction networks and the transcriptional regulatory networks with graph theoretic rigor, has led to the growing importance of an interdisciplinary approach [71]. Network centrality measures, which has been of interest to the social scientists, from as long as 1950 [13], is today studied extensively in the framework of complex networks. The thesis is an investigation on understanding human navigation with a network analytic approach using the well established and widely used centrality measures. Experiments were conducted on human participants to observe how people navigate in a complex environment. We made human participants way-find a destination from a source on a complex network and analyzed the paths that were taken. Our analysis established a fact that the learning process involved in navigating better in an unknown network boils down to learning certain strategic locations on the network. The vertices in the paths taken by the participants, when analyzed using the available centrality measures, enabled us to conclude experimentally that humans are naturally inclined to learn superior ranked vertices to navigate better and reach their intended destination. Our experiments were based on a word game called the word-morph. A generalized version of the experiment was conducted on a 6x6 photo collage with an underlying network hidden from the participant. A detailed analysis of the above experiment established a fact that, when humans are asked to take a goal-directed path, they were prone to take a path that passed through landmark nodes in the network. We call such paths center-strategic. We then present an algorithm that simulates the navigational strategy adopted by humans. We show empirically that the algorithm performs better than naive random walk based navigational techniques. We observe that the algorithm produces rich center-strategic paths on scale-free networks. We note that the effectiveness of the algorithm is highly dependent on the topology of the network by comparing its functionality on Erdos-Renyi networks and Barabasi-Albert networks. Then we discuss a lookahead algorithm to compute betweenness centrality in networks under vertex deletion operations. We show that the widely used Brandes algorithm can be modified to a lookahead version. We show that our proposed algorithm performs better than recomputing the betweenness centrality values in the vertex deleted graph. We show that our method works 20% faster than the Brandes algorithm.

Medidas de centralidades são um mecanismo importante para revelar informações vitais sobre redes complexas. No entanto, essas métricas exigem um alto custo computacional que prejudica a sua aplicação em grandes redes do mundo real. Em nosso estudo propomos e explicamos que através do uso de redes neurais artificiais podemos aplicar essas métricas em redes de tamanho arbitrário. Além disso, identificamos a melhor configuração e metodologia para otimizar a acurácia do aprendizado neural, além de apresentar uma maneira fácil de obter e gerar um número suficiente de dados de treinamento substanciais através do uso de um modelo de redes complexas que é adaptável a qualquer aplicação. Também realizamos um comparativo da técnica proposta com diferentes metodologias de aproximação de centralidade da literatura, incluindo métodos de amostragem e outros algoritmos de aprendizagem, e, testamos o modelo gerado pela rede neural em casos reais. Mostramos com os resultados obtidos em nossos experimentos que o modelo de regressão gerado pela rede neural aproxima com sucesso as métricas é uma alternativa eficiente para aplicações do mundo real. A metodologia e o modelo de aprendizagem de máquina que foi proposto usa apenas uma fração do tempo de computação necessário para os algoritmos de aproximação baseados em amostragem e é mais robusto que as técnicas de aprendizagem de máquina testadas / Centrality measures are an important analysis mechanism to uncover vital information about complex networks. However, these metrics have high computational costs that hinder their applications in large real-world networks. I propose and explain the use of artificial neural learning algorithms can render the application of such metrics in networks of arbitrary size. Moreover, I identified the best configuration and methodology for neural learning to optimize its accuracy, besides presenting an easy way to acquire and generate plentiful and meaningful training data via the use of a complex networks model that is adaptable for any application. In addition, I compared my prosed technique based on neural learning with different centrality approximation methods proposed in the literature, consisting of sampling and other artificial learning methodologies, and, I also tested the neural learning model in real case scenarios. I show in my results that the regression model generated by the neural network successfully approximates the metric values and is an effective alternative in real-world applications. The methodology and machine learning model that I propose use only a fraction of computing time with respect to other commonly applied approximation algorithms and is more robust than the other tested machine learning techniques.

Present day email is provided by centralized services running in the cloud. The services transparently connect users behind middleboxes and provide backup, redundancy, and high availability at the expense of user privacy. In present day mobile environments, users can access and modify email from multiple devices with updates reconciled on the central server. Prioritizing updates is difficult and may be undesirable. Moreover, legacy email protocols do not provide optimal email synchronization and access. Recent phenomena of the Internet of Things (IoT) will see the number of interconnected devices grow to 27 billion by 2021. In the first part of my dissertation I am proposing a decentralized email architecture which takes advantage of user's a IoT devices to maintain a complete email history. This addresses the email reconciliation issue and places data under user control. I replace legacy email protocols with a synchronization protocol to achieve eventual consistency of email and optimize bandwidth and energy usage. The architecture is evaluated on a Raspberry Pi computer. There is an extensive body of research on Social Network Analysis (SNA) based on email archives. Typically, the analyzed network reflects either communication between users or a relationship between the email and the information found in the email's header and the body. This approach discards either all or some email attachments that cannot be converted to text; for instance, images. Yet attachments may use up to 90% of an email archive size. In the second part of my dissertation I suggest extracting the network from email attachments shared between users. I hypothesize that the network extracted from shared email attachments might provide more insight into the social structure of the email archive. I evaluate communication and shared email attachments networks by analyzing common centrality measures and classication and clustering algorithms. I further demonstrate how the analysis of the shared attachments network can be used to optimize the proposed decentralized email architecture.

Modern societies are fully dependent on critical infrastructures networks to support the economy, security, and prosperity. Energy infrastructure network is of paramount importance to our societies. As a pillar of the economy, it is necessary that energy infrastructure networks continue to operate safely and be resilient to provide reliable power to other critical infrastructure networks. Nonetheless, frequent large-scale blackouts in recent years have highlighted the vulnerability in the power grids, where disruptions can trigger cascading failures causing a catastrophic regional-level blackout. Such catastrophic blackouts call for a systemic risk assessment approach whereby the entire network/system is assessed against such failures considering the dynamic power flow within. However, the lack of detailed data combining both topological and functional information, and the computational resources typically required for large-scale modelling, considering also operational corrective actions, have impeded large-scale resilience studies. In this respect, the research in the present dissertation focuses on investigating, analyzing, and evaluating the vulnerability of power grid infrastructure networks in an effort to enhance their resilience. Through a Complex Network Theory (CNT) lens, the power grid robustness has been evaluated against random and targeted attacks through evaluating a family of centrality measures. The results shows that CNT models provide a quick and potential indication to identify key network components, which support regulators and operators in making informed decisions to maintain and upgrade the network, constrained by the tolerable risk and allocated financial resources. Furthermore, a dynamic Cascade Failure Model (CFM) has been employed to develop a Physical Flow-Based Model (PFBM). The CFM considers the operational corrective actions in case of failure to rebalance the supply and demand (i.e., dispatch and load shedding). The CFM was subsequently utilized to construct a grid vulnerability map function of the Link Vulnerability Index (LVI), which can be used to rank the line maintenance priority. In addition, a Node Importance Index (NII) has been developed for power substations ranking according to the resulting cascade failure size. The results from CNT and CFM approaches were compared to address the impact of considering the physical behavior of the power grid. The comparison results indicate that relying solely on CNT topology-based model could result in erroneous conclusions pertaining to the grid behavior. Moving forward, a systemic risk mitigation strategy based on the Intentional Controlled Islanding (ICI) approach has been introduced to suppress the failure propagation. The proposed mitigation strategy integrated the operation- with structure-guided strategies has shown excellent capabilities in terms of enhancing the network robustness and minimizing the possibility of catastrophic large-scale blackouts. This research demonstrates the model application on a real large-scale network with data ranging from low to high voltage. In the future, the CFM model can be integrated with other critical infrastructure network systems to establish a network-of-networks interaction model for assessing the systemic risk throughout and between multiple network layers. Understanding the interdependence between different networks will provide stakeholders with insight on enhancing resilience and support policymakers in making informed decisions pertaining to the tolerable systemic risk level to take reliable actions under abnormal conditions. / Thesis / Doctor of Philosophy (PhD)

Centrality measures in network analysis have become a popular measurement tool for identifying coherent nodes within a network. In the context of stock markets, the centrality measure helps to identify key performing ele- ments and strengths for specific stocks and determine their impact on disrupting market value and performance. Multiple studies presented practical implementations of centrality measures for determining trends and perform- ance of a particular market. However, fewer studies applied centrality measures to predict trends in the stock market.

EUROPEAN AIR HUBS IN THE CONTEXT OF NETWORK AND ITS RESISTANCE AGAINST DISTURBANCES Abstract The submitted master thesis is addressing the theme of connectivity of European Air Transport Network, its properties and resistance against negative influences based on data from flight schedules for winter season 2018. The main objective of the thesis is to analyse European Air Transport Network from the point of connectivity in order to find out the most important airport hubs according to their geographic conditions, community structure and resistance of the whole network. Used methods are based on the Graph Theory and the centrality measures as indicators of connectivity. The empiric part of the thesis is divided into three parts. The aim of the first part is to find out, what airports are the most important in the European Air Transport Network. In the second part are explored properties and structure of the network. The last part is aiming to analyse the resistance of the European Air Transport Network from the view of robustness and resilience. Among the most important air hubs in Europe belong airports, that are serving world cities and tourist attractive localities. There is a strong dominance of the Schiphol airport in Amsterdam, the El Prat airport in Barcelona and the Frankfurt Airport. The European Air...