Nonparametric Relational Learning for Social Network Analysis

Social networks usually involve rich collections of objects, which are jointly linked into complex relational networks. Social network analysis has gained in importance due to the growing availability of data on novel social networks, e.g. citation networks, Web 2.0 social networks like facebook, and the hyperlinked internet. Recently, the infinite hidden relational model (IHRM) has been developed for the analysis of complex relational domains. The IHRM extends the expressiveness of a relational model by introducing for each object an infinite-dimensional hidden variable as part of a Dirichlet process mixture model. In this paper we discuss how the IHRM can be used to model and analyze social networks. In such an IHRM-based social network model, each edge is associated with a random variable (RV) and the probabilistic dependencies between these RVs are specified by the model based on the relational structure. The hidden variables, one for each object, are able to transport information such that non-local probabilistic dependencies can be obtained. The IHRM provides effective relationship prediction and cluster analysis for social networks. The experimental analysis is performed on two social network applications. The first application is an analysis of the cooperative effect in a recommendation framework where both user properties and item properties are taken into account. The experimental results demonstrate that the IHRM provides good prediction accuracy for user preference on movies and gives interpretable clusters of users and items. In the second experiment we apply the IHRM to Sampson’s monastery data, and obtain a grouping of the actors that agrees with results from previous publications. As an additional contribution of this paper, we present a new mean field approximation to inference in the IHRM. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. The 2nd SNA-KDD Workshop ’08 (SNA-KDD’08), August 24, 2008, Las Vegas, Nevada, USA Copyright 2008 ACM 978-1-59593-848-0 ...$5.00.