Tracking the Evolution of Communities in Dynamic Social Networks
Abstract
Real-world social networks from many domains can naturally be modelled as dynamic graphs. However, approaches for detecting communities have largely focused on identifying communities in static graphs. Therefore, 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 communities which
persist over time in dynamic networks, where each community is characterised
by a series of evolutionary events. Based on this model, we propose a scalable community-tracking strategy for efficiently identifying dynamic communities. Evaluations on a large number of synthetic graphs containing embedded
evolutionary events demonstrate that this strategy can successfully track communities over time in dynamic networks with different levels of volatility. We then
describe experiments to explore the evolving community structures present in real
mobile operator networks, represented by monthly call graphs for millions of subscribers.
begun to consider the problem of tracking the evolution of groups of users in
dynamic scenarios. Here we describe a model for tracking communities which
persist over time in dynamic networks, where each community is characterised
by a series of evolutionary events. Based on this model, we propose a scalable community-tracking strategy for efficiently identifying dynamic communities. Evaluations on a large number of synthetic graphs containing embedded
evolutionary events demonstrate that this strategy can successfully track communities over time in dynamic networks with different levels of volatility. We then
describe experiments to explore the evolving community structures present in real
mobile operator networks, represented by monthly call graphs for millions of subscribers.
Research Areas
