A plausible representation of relational information among entities in dynamic systems such as a living cell or a social community is a stochastic network which is topologically rewiring and semantically evolving over time. While there is a rich literature on modeling static or temporally invariant networks, much less has been done toward modeling the dynamic processes underlying rewiring networks, and on recovering such networks when they are not observable. We present a class of hidden temporal exponential random graph models (htERGMs) to study the yet unexplored topic of modeling and recovering temporally rewiring networks from time series of node attributes such as activities of social actors or expression levels of genes. We show that one can reliably infer the latent timespecific topologies of the evolving networks from the observation. We report empirical results on both synthetic data and a Drosophila lifecycle gene expression data set, in comparison with a static counterpart of htERGM. 1.

This article discusses a latent variable model for inference and prediction of symmetric relational data. The model, based on the idea of the eigenvalue decomposition, represents the relationship between two nodes as the weighted inner-product of node-specific vectors of latent characteristics. This “eigenmodel ” generalizes other popular latent variable models, such as latent class and distance models: It is shown mathematically that any latent class or distance model has a representation as an eigenmodel, but not vice-versa. The practical implications of this are examined in the context of three real datasets, for which the eigenmodel has as good or better out-of-sample predictive performance than the other two models. 1

Abstract: Humans are well known to belong to many associative groups simultaneously, with various levels of affiliation. However, most group detection algorithms for social networks impose a strict partitioning on nodes, forcing entities to belong to a single group. Link analysis research has produced several methods which detect multiple memberships but force equal membership. This paper extends these approaches by introducing the FOG framework, a stochastic model and group detection algorithm for fuzzy, overlapping groups. We apply our algorithm to both link data and network data, where we use a random walk approach to generate rich links from networks. The results demonstrate that not only can fuzzy groups be located, but also that the strength of membership in a group and the fraction of individuals with exclusive membership are highly informative of emerging group dynamics.

This paper addresses the problem of unsupervised group discovery in social networks. We adopt a nonparametric Bayesian framework that extends previous models to networks where the interacting objects can simultaneously belong to several groups (i.e., mixed membership). For this purpose, a hierarchical nonparametric prior is utilized and inference is performed using Gibbs sampling. The resulting mixed-membership model combines the usual advantages of nonparametric models, such as inference of the total number of groups from the data, and provides a more flexible modeling environment by quantifying the degrees of membership to the various groups. Such models are useful for social information processing because they can capture a user’s multiple interests and hobbies.

Every piece of textual data is generated as a method to convey its authors ’ opinion regarding specific topics. Authors deliberately organize their writings and create links, i.e., references, acknowledgments, for better expression. Thereafter, it is of interest to study texts as well as their relations to understand the underlying topics and communities. Although many efforts exist in the literature in data clustering and topic mining, they are not applicable to community discovery on large document corpus for several reasons. First, few of them consider both textual attributes as well as relations. Second, scalability remains a significant issue for large-scale datasets. Additionally, most algorithms rely on a set of initial parameters that are hard to be captured and tuned. Motivated by the aforementioned observations, a hierarchical

A general modeling framework is proposed that unifies nonparametric-Bayesian models, topic-models and Bayesian networks. This class of infinite state Bayes nets (ISBN) can be viewed as directed networks of ‘hierarchical Dirichlet processes ’ (HDPs) where the domain of the variables can be structured (e.g. words in documents or features in images). We show that collapsed Gibbs sampling can be done efficiently in these models by leveraging the structure of the Bayes net and using the forward-filtering-backward-sampling algorithm for junction trees. Existing models, such as nested-DP, Pachinko allocation, mixed membership stochastic block models as well as a number of new models are described as ISBNs. Two experiments have been performed to illustrate these ideas. 1

A plausible representation of the relational information among entities in dynamic systems such as a living cell or a social community is a stochastic network which is topologically rewiring and semantically evolving over time. While there is a rich literature on modeling static or temporally invariant networks, until recently, little has been done toward modeling the dynamic processes underlying rewiring networks, and on recovering such networks when they are not observable. In this paper we present an optimization-based approach for recovering time-evolving discrete networks from time stamped node samples from the network. We cast this graphical model learning problem as a temporally smoothed L1-regularized logistic regression problem which can be formulated and solved efficiently using standard convex-optimization solvers scalable to large networks. We report promising results on recovering the dynamics of the coauthorship-keyword academic In many problems arising in social, biological, and other fields, it is often necessary to analyze populations of individuals (actors), interconnected by a set of relationships (e.g., friendship, communication, influence, etc.) represented as a network. Real-time analysis of network data is important for detecting anomaly, predicting vulnerability, and assessing the potential impact of

This thesis deals with the problem of constructing social networks with topic modeling. To be more specific, it describes how we identify the local communities where people share same interests as well as link this groups together to construct social networks. These problems will be addressed in the course of discussion, and corresponding practical solutions will be provided. I will present the system which fully implements the ideas I propose, in order to achieve the goal. i Acknowledgements I would like to offer my sincerest gratitude to my supervisor, Michael Fourman for his continuous support and encouragement throughout this project. He spent hours discussing with me about the project for both theoretical and practical issues, and ever refused to devote this precious time and intelligence to answering kinds of questions. A special thanks goes to my friend Theodosia Togia, who guide me through my first steps in writing the dissertation and alway there when I need someone to talk to. Many thanks to my friends Cheng Cheng and Ran Cheng for the encouragement they offered to me. I also want to express my thanks to my girlfriend who always comforted me

The focus of this paper is an approach to the modeling of longitudinal social network or relational data. Such data arise from measurements on pairs of objects or actors made at regular temporal intervals, resulting in a social network for each point in time. In this article we represent the network and temporal dependencies with a random effects model, resulting in a stochastic process defined by a set of stationary covariance matrices. Our approach builds upon the social relations models of Warner, Kenny and Stoto [Journal of Personality and Social Psychology 37 (1979) 1742–1757] and Gill and Swartz [Canad. J. Statist. 29 (2001) 321–331] and allows for an intra- and inter-temporal representation of network structures. We apply the methodology to two longitudinal data sets: international trade (continuous response) and militarized interstate disputes (binary response).

In a dynamic social or biological environment, the interactions between the underlying actors can undergo large and systematic changes. The latent roles or membership of the actors as determined by these dynamic links will also exhibit rich temporal phenomena, assuming a distinct role at one point while leaning more towards a second role at an another point. To capture this dynamic mixed membership in rewiring networks, we propose a state space mixed membership stochastic blockmodel which embeds an actor into a latent space and track its mixed membership in the latent space across time. We derived efficient approximate learning and inference algorithms for our model, and applied the learned models to analyze a social network between monks, and a rewiring gene interaction network of Drosophila melanogaster collected during its full life cycle. In both cases, our model reveals interesting patterns of the dynamic roles of the actors. 1. Introduction. Inference of network tomography