An Ensemble Framework for Detecting Community Changes in Dynamic Networks

TL;DR

This paper introduces an ensemble clustering framework for dynamic networks that effectively captures community evolution over time, improving detection accuracy and providing clear visualizations of community changes.

Contribution

It proposes an ensemble approach to dynamic community detection using a dynamic stochastic block model, enhancing accuracy and interpretability over existing methods.

Findings

Ensemble clustering improves pairwise-precision and recall.

The method effectively visualizes community evolution as a flowchart.

Dynamic models outperform static models in capturing community changes.

Abstract

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.