Tracking communities over time in dynamic social network

Abstract

This poster paper presents an approach for tracking community structures. In contrast to the vast majority of existing methods, which are based on time-to-time consecutive evaluation, the proposed approach uses a similarity measure that involves the global temporal aspect of the network under investigation. A notable feature of our approach is that it is able to preserve the generated content across different time points. To demonstrate the suitability of the proposed method, we conducted experiments on real data extracted from the DBLP.