Meta Path-Based Information Entropy for Modeling Social Influence in Heterogeneous Information Networks

Abstract

Influence is a complex and subtle force that changes the behavior of involved users. Measuring influence can benefit to identify the influential users, and also benefit to provide important insights into the design of social platforms and applications. However, most existing work on social influence analysis has focused on homogeneous information networks. Few studies systematically investigate how to mine the strength of influence between nodes in heterogeneous information networks. In this paper, we present a meta path-based information entropy for modeling social influence in heterogeneous information networks (MPIE). Through setting meta paths, MPIE not only flexibly integrates heterogeneous information, but also obtains potential link information to measure the influence of nodes. Experiments on real data sets demonstrate the effectiveness of our proposed method.