An Ensemble Framework for Detecting Community Changes in Dynamic Networks
This work addresses the challenge of tracking community changes over time in social networks, offering an incremental improvement over existing dynamic models.
The paper tackles the problem of detecting evolving community structures in dynamic networks by proposing an ensemble framework that improves detection accuracy, achieving superior results in pairwise-precision and pairwise-recall metrics.
Dynamic networks, especially those representing social networks, undergo constant evolution of their community structure over time. Nodes can migrate between different communities, communities can split into multiple new communities, communities can merge together, etc. In order to represent dynamic networks with evolving communities it is essential to use a dynamic model rather than a static one. Here we use a dynamic stochastic block model where the underlying block model is different at different times. In order to represent the structural changes expressed by this dynamic model the network will be split into discrete time segments and a clustering algorithm will assign block memberships for each segment. In this paper we show that using an ensemble of clustering assignments accommodates for the variance in scalable clustering algorithms and produces superior results in terms of pairwise-precision and pairwise-recall. We also demonstrate that the dynamic clustering produced by the ensemble can be visualized as a flowchart which encapsulates the community evolution succinctly.