Now showing 1 - 2 of 2
  • Publication
    Tracking the Evolution of Communities in Dynamic Social Networks
    (University College Dublin. School of Computer Science and Informatics, 2011-05) ; ;
    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.
      218
  • Publication
    Tracking the evolution of communities in dynamic social networks
    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.
      4572Scopus© Citations 400