Link label prediction is the problem of predicting the missing labels or signs of all the unlabeled edges in a network. For signed networks, these labels can either be positive or negative. In recent years, different algorithms have been proposed such as using regression, trust propagation and matrix factorization. These approaches have tried to solve the problem of link label prediction by using ideas from social theories, where most of them predict a single missing label given that labels of other edges are known. However, in most real-world social graphs, the number of labeled edges is usually less than that of unlabeled edges. Therefore, predicting a single edge label at a time would require multiple runs and is more computationally demanding. In this thesis, we look at link label prediction problem on a signed citation network with missing edge labels. Our citation network consists of papers from three major machine learning and data mining conferences together with their references, and edges showing the relationship between them. An edge in our network is labeled either positive (dataset relevant) if the reference is based on the dataset used in the paper or negative otherwise. We present three approaches to predict the missing labels. The first approach converts the label prediction problem into a standard classification problem. We then, generate a set of features for each edge and then adopt Support Vector Machines in solving the classification problem. For the second approach, we formalize the graph such that the edges are represented as nodes with links showing similarities between them. We then adopt a label propagation method to propagate the labels on known nodes to those with unknown labels. In the third approach, we adopt a PageRank approach where we rank the nodes according to the number of incoming positive and negative edges, after which we set a threshold. Based on the ranks, we can infer an edge would be positive if it goes a node above the threshold. Experimental results on our citation network corroborate the efficacy of these approaches. With each edge having a label, we also performed additional network analysis where we extracted a subnetwork of the dataset relevant edges and nodes in our citation network, and then detected different communities from this extracted sub-network. To understand the different detected communities, we performed a case study on several dataset communities. The study shows a relationship between the major topic areas in a dataset community and the data sources in the community.

Nous abordons dans cette thèse quelques-unes des questions soulevées par I'émergence d'applications sociales sur le Web, en se concentrant sur deux axes importants: l'efficacité de recherche sociale dans les applications Web et l'inférence de liens sociaux signés à partir des interactions entre les utilisateurs dans les applications Web collaboratives. Nous commençons par examiner la recherche sociale dans les applications de "tag- ging". Ce problème nécessite une adaptation importante des techniques existantes, qui n'utilisent pas des informations sociaux. Dans un contexte ou le réseau est importante, on peut (et on devrait) d'exploiter les liens sociaux, ce qui peut indiquer la façon dont les utilisateurs se rapportent au demandeur et combien de poids leurs actions de "tagging" devrait avoir dans le résultat. Nous proposons un algorithme qui a le potentiel d'évoluer avec la taille des applications actuelles, et on le valide par des expériences approfondies. Comme les applications de recherche sociale peut être considérée comme faisant partie d'une catégorie plus large des applications sensibles au contexte, nous étudions le problème de répondre aux requêtes à partir des vues, en se concentrant sur deux sous-problèmes importants. En premier, la manipulation des éventuelles différences de contexte entre les différents points de vue et une requête d'entrée conduit à des résultats avec des score incertains, valables pour le nouveau contexte. En conséquence, les algorithmes top-k actuels ne sont plus directement applicables et doivent être adaptés aux telle incertitudes dans les scores des objets. Deuxièmement, les techniques adaptées de sélection de vue sont nécessaires, qui peuvent s’appuyer sur les descriptions des requêtes et des statistiques sur leurs résultats. Enfin, nous présentons une approche pour déduire un réseau signé (un "réseau de confiance") à partir de contenu généré dans Wikipedia. Nous étudions les mécanismes pour deduire des relations entre les contributeurs Wikipédia - sous forme de liens dirigés signés - en fonction de leurs interactions. Notre étude met en lumière un réseau qui est capturée par l’interaction sociale. Nous examinons si ce réseau entre contributeurs Wikipedia représente en effet une configuration plausible des liens signes, par l’étude de ses propriétés globaux et locaux du reseau, et en évaluant son impact sur le classement des articles de Wikipedia. / We address in this thesis some of the issues raised by the emergence of social applications on the Web, focusing on two important directions: efficient social search inonline applications and the inference of signed social links from interactions between users in collaborative Web applications. We start by considering social search in tagging (or bookmarking) applications. This problem requires a significant departure from existing, socially agnostic techniques. In a network-aware context, one can (and should) exploit the social links, which can indicate how users relate to the seeker and how much weight their tagging actions should have in the result build-up. We propose an algorithm that has the potential to scale to current applications, and validate it via extensive experiments. As social search applications can be thought of as part of a wider class of context-aware applications, we consider context-aware query optimization based on views, focusing on two important sub-problems. First, handling the possible differences in context between the various views and an input query leads to view results having uncertain scores, i.e., score ranges valid for the new context. As a consequence, current top-k algorithms are no longer directly applicable and need to be adapted to handle such uncertainty in object scores. Second, adapted view selection techniques are needed, which can leverage both the descriptions of queries and statistics over their results. Finally, we present an approach for inferring a signed network (a "web of trust")from user-generated content in Wikipedia. We investigate mechanisms by which relationships between Wikipedia contributors - in the form of signed directed links - can be inferred based their interactions. Our study sheds light into principles underlying a signed network that is captured by social interaction. We investigate whether this network over Wikipedia contributors represents indeed a plausible configuration of link signs, by studying its global and local network properties, and at an application level, by assessing its impact in the classification of Wikipedia articles.javascript:nouvelleZone('abstract');_ajtAbstract('abstract');

This dissertation focuses on studying various network formation games in Economics. We explore a different model in each chapter to capture various aspects of networks. Chapter 1provides an overview of this dissertation. Chapter 2 studies the possible Nash equilibrium configurations in a model of signed network formation as proposed by Hiller (2017). We specify the Nash equilibria in the case of heterogeneous agents. We find 3 possible Nash equilibrium configurations: Utopia network, positive assortative matching, and disassortative matching. We derive the specific conditions under which they arise in a Nash equilibrium. In Chapter 3, we study a generalized model of signed network formation game where the players can choose not only positive and negative links but also neutral links. We check whether the results of the signed network formation model in the literature still hold in our generalized framework using the notion of pairwise Nash equilibrium. Chapter 4 studies inequality in a weighted network formation model using the notion of Nash equilibrium. As a factor of inequality, there are two types of players: Rich players and poor players. We show that both rich and poor players designate other rich players as their best friends. As a result, We present that nested split graphs are drawn from survey data because researchers tend to ask respondents to list only a few friends. / Doctor of Philosophy / This dissertation focuses on studying various network formation games in Economics. We explore a different model in each chapter to capture various aspects of networks. Chapter 1 provides an overview of this dissertation. Chapter 2 studies the possible singed network configurations in equilibrium. In the signed network, players can choose a positive (+) relationship or a negative (-) relationship toward each other player. We study the case that the players are heterogeneous. We find 3 possible categories of networks in equilibrium: Utopia network, positive assortative matching, and disassortative matching. We derive the specific conditions under which they arise in equilibrium. In Chapter 3, we study a generalized model of signed network formation game where the players can choose not only positive and negative links but also neutral links. We check whether the results of the signed network formation model in the literature still hold in our generalized framework. Chapter 4 studies inequality in a weighted network formation model using the notion of Nash equilibrium. In this weighted network model, each player can choose the level of relationship. As a factor of inequality, there are two types of players: rich players and poor players. We show that both rich and poor players choose other rich players as their best friends. As a result, we present that nested split graphs are drawn from survey data because these social network data are censored due to the limit of the number of responses.