Structural Analysis in Multi-Relational Social Networks

Abstract

Modern social networks often consist of multiple relations among individuals. Understanding the structure of such multi-relational network is essential. In sociology, one way of structural analysis is to identify different positions and roles using blockmodels. In this paper, we generalize stochastic blockmodels to Generalized Stochastic Blockmodels (GSBM) for performing positional and role analysis on multi-relational networks. Our GSBM generalizes many different kinds of Multivariate Probability Distribution Function (MVPDF) to model different kinds of multi-relational networks. In particular, we propose to use multivariate Poisson distribution for multi-relational social networks. Our experiments show that GSBM is able to identify the structures for both synthetic and real world network data. These structures can further be used for predicting relationships between individuals.

Churn Influence Diffusion in a Multi-Relational Call Network

“…In this study we will focus on the heterogeneous nature that truly characterizes the telecommunication network to analyze the spread of churn influence.Most of the existing studies on social networks consider homogeneity in the analysis of the ties rather than heterogeneity [15,16]. However, the relationships among people are diversified.…”