Mining Evolutionary Link Strength for Information Diffusion Modeling in Online Social Networks

Abstract

In this paper, we consider the problem of evolutionary link strength mining for information diffusion modeling and predicting. Most exiting approaches analyzing link strength focus on either topological information or content semantics, but do not simultaneously consider combining both features. Furthermore, in many applications of text stream data, the users are always in need of the ability to track link strength evolution pattern along time line. In this paper, we argue that the evolutionary link strength reflects a user’s interest to create reply links and is influenced by both neighbors and topic content. With this aim in mind, we propose a novel Content-Topology-Time model for link strength predicting in time-aware online social network. Comprehensive experimental studies on real world micro-blog data set show that our approach outperforms existing ones and well matches the practice.