A Game theoretic approach for competition over visibility in social networks

Social Networks have known an important evolution in the last few years. These structures, made up of individuals who are tied by one or more specific types of interdependency, constitute the window for members to express their opinions and thoughts by sending posts to their own walls or others' timelines. Actually, when a content arrives, it's located on the top of the timeline pushing away older messages. This situation causes a permanent competition over visibility among subscribers who jump on opponents to promote conflict. Our study presents this competition as a non-cooperative game; each source has to choose frequencies which assure its visibility. We model it, exploring the theory of concave games, to reach a situation of equilibrium; a situation where no player has the ultimate ability to deviate from its current strategy. We formulate the named game, then we analyze it and prove that there is exactly one Nash equilibrium which is the convergence of all players' best responses. We finally provide some numerical results, taking into consideration a system of two sources with a specific frequency space, and analyze the effect of different parameters on sources' visibility on the walls of social networks

One size does not fit all : profiling personalized time-evolving user behaviors

Given the set of social interactions of a user, how can we detect changes in interaction patterns over time? While most previous work has focused on studying network-wide properties and spotting outlier users, the dynamics of individual user interactions remain largely unexplored. This work sets out to explore those dynamics in a way that is minimally invasive to privacy, thus, avoids to rely on the textual content of user posts---except for validation. Our contributions are two-fold. First, in contrast to previous studies, we challenge the use of a fixed interval of observation. We introduce and empirically validate the "Temporal Asymmetry Hypothesis", which states that appropriate observation intervals should vary both among users and over time for the same user. We validate this hypothesis using eight different datasets, including email, messaging, and social networks data. Second, we propose iNET, a comprehensive analytic and visualization framework which provides personalized insights into user behavior and operates in a streaming fashion. iNET learns personalized baseline behaviors of users and uses them to identify events that signify changes in user behavior. We evaluate the effectiveness of iNET by analyzing more than half a million interactions from Facebook users. Labeling of the identified changes in user behavior showed that iNET is able to capture a wide spectrum of exogenous and endogenous events, while the baselines are less diverse in nature and capture only 66% of that spectrum. Furthermore, iNET exhibited the highest precision (95%) compared to all competing approaches

Modelação comportamental em redes sociais

Mestrado em Engenharia de Computadores e TelemáticaAs redes sociais têm tido um crescimento viral nos últimos anos. No início do século XXI já se discutia a indispensabilidade da Internet e no presente, as redes sociais reforçam ainda mais esta ideia. O ser humano, ao longo da sua história, foi mostrando a necessidade de exprimir as suas ideias, os seus pensamentos, as suas alegrias, as suas tristezas… As redes sociais são assim um espaço onde as pessoas, de diferentes idades ou culturas, podem partilhar os seus pensamentos e experiências. As redes sociais são desta forma um espaço apetecível para todo o tipo de ataques informáticos, especialmente de phishing. Nesta dissertação faz-se uma análise de diferentes redes sociais, das suas APIs e das formas de extrair informação das mesmas, dando especial enfâse ao Facebook. Como tal, foi desenvolvido uma ferramenta que utiliza esta informação e que permite monitorizar o comportamento de um utilizador, permitindo a verificação da legitimidade do seu comportamento. Neste projeto foi utilizada a Graph API do Facebook, que se trata de uma API baseada no protocolo HTTP e que permite aceder à estrutura social (Social Graph) do Facebook, retornando os dados no formato JSON. Para fazer a ligação ao Facebook foi utilizado o Facebook PHP SDK. O script utilizado é independente do website e guarda toda a informação em JSON, estando os ficheiros organizados por tipo de conteúdo e pelo ID do utilizador. Desta forma o script pode ser facilmente reutilizado para outro tipo de ferramentas online ou offline.Social networks are having a viral growth in recent years. The vital importance of the Internet has been under discussion since the beginning of the 21st century and social networks are reinforcing this idea. The human being, throughout its history, has been showing the need to express their ideas, their thoughts, their joys, their sorrows ... Social networks become then a tool which people, of different ages and cultures, can use for sharing their thoughts and experiences. Social networks are an attractive place to all kinds of cyber-attacks, especially phishing. This dissertation analyzes different social networks, their APIs and how to extract information from them, giving more emphasis to Facebook. As such, a website was developed that uses this information and transforms it into a tool that allows users to monitor their behavior and to verify if it is legitimate. In this model we used the Facebook Graph API, which is an HTTP based API that allows access to the Facebook Social Graph, returning data in JSON format. To connect to Facebook, the Facebook PHP SDK was used. The script is independent from the website and keeps all the information in JSON files that are organized by content type and user ID. In this manner, the script can be easily reused for other type of tools, online or offline

Sparsity-aware neural user behavior modeling in online interaction platforms

Modern online platforms offer users an opportunity to participate in a variety of content-creation, social networking, and shopping activities. With the rapid proliferation of such online services, learning data-driven user behavior models is indispensable to enable personalized user experiences. Recently, representation learning has emerged as an effective strategy for user modeling, powered by neural networks trained over large volumes of interaction data. Despite their enormous potential, we encounter the unique challenge of data sparsity for a vast majority of entities, e.g., sparsity in ground-truth labels for entities and in entity-level interactions (cold-start users, items in the long-tail, and ephemeral groups). In this dissertation, we develop generalizable neural representation learning frameworks for user behavior modeling designed to address different sparsity challenges across applications. Our problem settings span transductive and inductive learning scenarios, where transductive learning models entities seen during training and inductive learning targets entities that are only observed during inference. We leverage different facets of information reflecting user behavior (e.g., interconnectivity in social networks, temporal and attributed interaction information) to enable personalized inference at scale. Our proposed models are complementary to concurrent advances in neural architectural choices and are adaptive to the rapid addition of new applications in online platforms. First, we examine two transductive learning settings: inference and recommendation in graph-structured and bipartite user-item interactions. In chapter 3, we formulate user profiling in social platforms as semi-supervised learning over graphs given sparse ground-truth labels for node attributes. We present a graph neural network framework that exploits higher-order connectivity structures (network motifs) to learn attributed structural roles of nodes that identify structurally similar nodes with co-varying local attributes. In chapter 4, we design neural collaborative filtering models for few-shot recommendations over user-item interactions. To address item interaction sparsity due to heavy-tailed distributions, our proposed meta-learning framework learns-to-recommend few-shot items by knowledge transfer from arbitrary base recommenders. We show that our framework consistently outperforms state-of-art approaches on overall recommendation (by 5% Recall) while achieving significant gains (of 60-80% Recall) for tail items with fewer than 20 interactions. Next, we explored three inductive learning settings: modeling spread of user-generated content in social networks; item recommendations for ephemeral groups; and friend ranking in large-scale social platforms. In chapter 5, we focus on diffusion prediction in social networks where a vast population of users rarely post content. We introduce a deep generative modeling framework that models users as probability distributions in the latent space with variational priors parameterized by graph neural networks. Our approach enables massive performance gains (over 150% recall) for users with sparse activities while being faster than state-of-the-art neural models by an order of magnitude. In chapter 6, we examine item recommendations for ephemeral groups with limited or no historical interactions together. To overcome group interaction sparsity, we present self-supervised learning strategies that exploit the preference co-variance in observed group memberships for group recommender training. Our framework achieves significant performance gains (over 30% NDCG) over prior state-of-the-art group recommendation models. In chapter 7, we introduce multi-modal inference with graph neural networks that captures knowledge from multiple feature modalities and user interactions for multi-faceted friend ranking. Our approach achieves notable higher performance gains for critical populations of less-active and low degree users