Tracking the Evolution of Communities in Dynamic Social Networks

TLDR

Dynamic social networks can be modeled as evolving graphs, yet community detection has largely focused on static graphs, prompting recent efforts to track group evolution over time. The study proposes a model to track community progress over time by characterizing each community through a series of significant evolutionary events. Using this model, the authors develop a community‑matching strategy that efficiently identifies and tracks dynamic communities, and evaluate it on synthetic and large‑scale mobile operator networks. Experiments on synthetic graphs show the strategy accurately tracks communities in volatile networks, and real‑world tests confirm its applicability to millions‑user mobile operator data.

Abstract

Real-world social networks from a variety of domains can naturally be modelled as dynamic graphs. However, approaches to detecting communities have largely focused on identifying communities in static graphs. Recently, researchers have begun to consider the problem of tracking the evolution of groups of users in dynamic scenarios. Here we describe a model for tracking the progress of communities over time in a dynamic network, where each community is characterised by a series of significant evolutionary events. This model is used to motivate a community-matching strategy for efficiently identifying and tracking dynamic communities. Evaluations on synthetic graphs containing embedded events demonstrate that this strategy can successfully track communities over time in volatile networks. In addition, we describe experiments exploring the dynamic communities detected in a real mobile operator network containing millions of users.