Predictive Analysis of Temporal and Overlapping Community Structures in Social Media

Abstract

Digital media has some observable traces named communities. Several events such as split, merge, dissolve and survive happen to communities in social media. But what are significant features to predict these events? And to which extent a feature is relevant in a social media? To answer these questions, we perform a study on community evolution analysis and prediction. We employ three overlapping community detection (OCD) algorithms from literature to the case of time-evolving networks including social, email communication and co-authorship networks. Group evolution discovery (GED) technique is applied to track the identified communities. We compare structural properties of OCD algorithms and investigate most persistent communities over time. Furthermore, static and temporal features of a community are applied to build a logistic classifier for community evolution prediction (CEP). Results reveal important features to predict events happening to a community.