Statistical analysis of network data is an active field of study, in which researchers inves- tigate graph-theoretic concepts and various probability models that explain the behaviour of real networks. This thesis attempts to combine two of these concepts: an exponential random graph and a centrality index. Exponential random graphs comprise the most useful class of probability models for network data. These models often require the assumption of a complex dependence structure, which creates certain difficulties in the estimation of unknown model parameters. However, in the context of dynamic networks the exponential random graph model provides the opportunity to incorporate a complex network structure such as centrality without the usual drawbacks associated with parameter estimation. The thesis employs this idea by proposing probability models that are equivalent to the logistic regression models and that can be used to explain behaviour of both static and dynamic networks.

Signals convey information to marketplace participants regarding the unobservable quality of a product. Whenever product quality if unobservable prior to purchase, there is the risk of adverse selection. Problems of hidden information also occur in the consumer marketplace when the consumer is unable to verify the quality of a good prior to purchase. The sending, receiving, and interpretation or signals are potential ways to overcome the problem of adverse selection. In general, there is a lack of empirical evidence for signaling hypothesis, particularly that which links signaling to business performance outcomes. This research proposes that reputation serves as a marketplace signal to convey unobservable information about products offered for sale. Signaling hypotheses are tested in a network context, examining the influence of signals throughout a network of buyers and sellers in a marketplace. There are many situations where a signal does not affect just one sender and one receiver; multiple constituencies may be aware of and react to a given signal. This study incorporates the actions of seller side principals, seller side agents, and buyer side agents when examining marketplace signals and provides a new perspective and better vantage point from which to test signaling theory. The research setting for this study is the world’s largest individual marketplace for Thoroughbred yearlings. Several sources of secondary data are employed. These openly available published sources of information were selected as representative of the information that would typically be available to marketplace principals and agents to use in planning interactions in this unique live auction marketplace. The findings from his study indicate that the reputation of seller side principals and agents affect the eventual business performance outcomes as measured by final price brought at auction for goods. Specifically, seller side principals and agents who have developed a reputation for producing or selling high-priced or high-performing goods will be rewarded in the marketplace with relatively higher prices for their goods. Buyer side agents who are more central in the marketplace will pay relatively higher prices for goods. Evidence suggests that more central seller side agents will receive relatively higher prices for their goods.

Purpose - The purpose of this study is to examine and map out the network innovative companies and to calculate values of the network structure in order to compare them to different performance measures. Furthermore, we aim to investigate the trade-off efficiency of innovations in a particular network structure, more specifically to investigate if the same elements generating more innovations have a relationship with economic performance that originates from innovations.   Methodology - This study give emphasis to map and illustrate the Swedish companies on NASDAQ OMX First North network through direct and indirect connection and to compare the centrality, density and size of the companies ego network in our population with the performance measures which are logically connected with the launch of an innovation; average EBITDA (earnings before interest, taxes, depreciation and amortization) and average annual turnover. Findings - First we noticed that there was a significant connection between a negative average EBITDA and positive average annual turnover for our population, as we foretold would occur during the launch of an innovation. Secondly, the paper suggests that there is a weak or near non-existent connection between the elements that generates more innovations and the result of innovations, e.g. the economic performance of innovative firms. This might indicate that the focus of recent studies in the subject might have been mistaken focusing on the quantity of innovation, when the basic assumption of an innovation is that it is qualitative and thereby generates money for the company. This study suggests that more innovations do not necessarily lead to better economic performance for the companies within our population.

Statistical analysis of network data is an active field of study, in which researchers inves- tigate graph-theoretic concepts and various probability models that explain the behaviour of real networks. This thesis attempts to combine two of these concepts: an exponential random graph and a centrality index. Exponential random graphs comprise the most useful class of probability models for network data. These models often require the assumption of a complex dependence structure, which creates certain difficulties in the estimation of unknown model parameters. However, in the context of dynamic networks the exponential random graph model provides the opportunity to incorporate a complex network structure such as centrality without the usual drawbacks associated with parameter estimation. The thesis employs this idea by proposing probability models that are equivalent to the logistic regression models and that can be used to explain behaviour of both static and dynamic networks.

An urban environment can be abstracted in form of a street network in order to be further analysed structurally. The urban street network can be represented in various ways by taking different principles and constraints into account. Therefore the aim of this work is to investigate human behaviour and communication in emerged urban phenomena, namely traffic flow and wayfinding, by structural characterization of an appropriate representation of an urban street network and modifying the conventional methods. / In order to characterize the depicted urban street network, centrality measure and specifically betweenness centrality is utilized. This analysis is then implemented to characterize the studied urban phenomena with respect to their structural, temporal and dynamic properties. In case of studying only the structural properties of the phenomena such as route description or self localization the conventional betweenness centrality is performed. But in case of studying the dynamic and temporal properties of a phenomenon such as traffic flow a modified version of betweenness centrality is proposed which considers dynamic and temporal aspects of human travel behaviour. / Experiments are designed to test the implementation of the suggested methods in the studied urban phenomena. The results of experiments demonstrate the efficiency of the proposed model in characterization of the studied urban phenomena in this thesis and then mention some of the problems and potential areas for future works.

In this thesis we investigate two topics in data mining on graphs; in the first part we investigate the notion of centrality in graphs, in the second part we look at reconstructing graphs from aggregate information. In many graph related problems the goal is to rank nodes based on an importance score. This score is in general referred to as node centrality. In Part I. we start by giving a novel and more efficient algorithm for computing betweenness centrality. In many applications not an individual node but rather a set of nodes is chosen to perform some task. We generalize the notion of centrality to groups of nodes. While group centrality was first formally defined by Everett and Borgatti (1999), we are the first to pose it as a combinatorial optimization problem; find a group of k nodes with largest centrality. We give an algorithm for solving this optimization problem for a general notion of centrality that subsumes various instantiations of centrality that find paths in the graph. We prove that this problem is NP-hard for specific centrality definitions and we provide a universal algorithm for this problem that can be modified to optimize the specific measures. We also investigate the problem of increasing node centrality by adding or deleting edges in the graph. We conclude this part by solving the optimization problem for two specific applications; one for minimizing redundancy in information propagation networks and one for optimizing the expected number of interceptions of a group in a random navigational network. In the second part of the thesis we investigate what we can infer about a bipartite graph if only some aggregate information -- the number of common neighbors among each pair of nodes -- is given. First, we observe that the given data is equivalent to the dot-product of the adjacency vectors of each node. Based on this knowledge we develop an algorithm that is based on SVD-decomposition, that is capable of almost perfectly reconstructing graphs from such neighborhood data. We investigate two versions of this problem, in the versions the dot-product of nodes with themselves, e.g. the node degrees, are either known or hidden.

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.

Orientador: Arthur Magon Whitacker / Banca: Everaldo Santos Melazzo / Banca: Sandra Lencioni / Resumo: Essa pesquisa teve como objetivo realizar uma análise do processo de reestruturação urbana, enfatizando as mudaças geradas na centralidade urbana. Para isso, tomamos como recorte territorial o município de Itu - SP. Desse modo, buscamos compreender as transformações que se processam na área central do município de Itu e que podem ser observadas na relação entre as formas herdadas e as novas dinâmicas que lhe são impostas pelo desenvolvimento econômico, pela expansão de seu tecido urbano e pelo crescimento populacional. Essas novas dinâmicas modificam o centro, atribuindo-lhe novos conteúdos, identificáveis, entre outros, pela intensificação de seus fluxos, implicando em novas centralidades. Com isso, gera-se o descompasso entre formas e funções no centro histórico. Procuramos apreender essas transformações indentificando suas conseqüências no plano da morfologia urbana, por meio da criação de novas centralidades. Para o desenvolvimento dessa pesquisa, priorizamos o enfoque econômico, por meio de análise das empresas existentes em nossa área de estudo. Os dados evidenciaram aspectos do centro que permitiram um maior entendimento de sua dinâmica e estrutura, bem como o conteúdo informacional existente nas novas centralidades que se configuram. / Abstract: This research had the objective to achieve an analyze of the restructuring urban process, emphasizing the changes generated in the centrality urban. For this, we assumed as territories bound the Itu municipality - SP. This way, we tried to understand the changes that occurs in the central area of the Itu municipality and it can be observed in the relation between the inherited forms and the new dynamics which are attributed for the economic development, for the urban tissue's expansion and for the population increase. These new dynamics change the center, attributing to it new contents, identified, among others, for the intensification of its flows, resulting in news centralities. So, generate itself the incompatibility between forms and functions in the historical center. We tried to apprehend these changes identifying its consequences in the urban morphology's plane, through the creation of news centralities. For this research's development, we priorited the economic point, through the analyze of the companies existent in our study area. The datuns evidenced centre's aspects that allowed a greater understanding of its dynamic and structure, as well as the informational content existent in the new centralities that configures itself. / Mestre

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.