Community Event Prediction in Dynamic Social Networks

Abstract

Communities are fundamental units of every social network, their structure and evolution are essential to understanding the structure and functionality of large networks. Also, community evolution prediction is an important task with various real-life applications in social network analysis. In this paper, we present a framework for modeling community evolution prediction in social networks. Each community is characterized by a wide range of structural features to describe community characteristics and a series of evolutionary events. A community matching algorithm is also proposed to efficiently identify and track similar communities over time. Experiments on different data sets prove that a high rate of community evolution prediction has been achieved.