Social network mining with nonparametric relational models

Statistical relational learning (SRL) provides effective techniques to analyze social network data with rich collections of objects and complex networks. Infinite hidden relational models (IHRMs) introduce nonparametric mixture models into relational learning and have been successful in many relational applications. In this paper we explore the modeling and analysis of complex social networks with IHRMs for community detection, link prediction and product recommendation. In an IHRM-based social network model, each edge is associated with a random variable and the probabilistic dependencies between these random variables 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 model can be used to predict entity attributes, to predict relationships between entities and it performs an interpretable cluster analysis. We demonstrate the performance of IHRMs with three social network applications. We perform community analysis on the Sampson’s monastery data and perform link analysis on the Bernard & Killworth data. Finally we apply IHRMs to the MovieLens data for prediction of user preference on movies and for an analysis of user clusters and movie clusters