Community evolution in temporal networks

Abstract

The purpose of this research is to study the structure of social networks with an added temporal element. Specifically, we examine dynamic community behavior within social networks. We base our experiments on a simple theoretical foundation which allows us to efficiently identify dynamic community evolutions. Based on this framework, we empirically study evolutions in large social networks and structural features of evolutions across all networks. Results show that structural properties remain similar across multiple social networks and it is possible to correlate the lifespan of a community to specific features of its early evolution.

We also develop a framework for generating social networks with structures similar to those found in real world systems. Using this framework, we examine the behavior of evolution detection algorithms in full networks and more isolated situations. Finally we examine the robustness of our developed community evolution tracking framework in noisy systems.