Identifying Community Structures in Dynamic Networks

Abstract

Most real-world social networks are inherently dynamic, composed of communities that are constantly changing in membership. To track these evolving communities, we need dynamic community detection techniques. This article evaluates the performance of a set of game theoretic approaches for identifying communities in dynamic networks. Our method, D-GT (Dynamic Game Theoretic community detection), models each network node as a rational agent who periodically plays a community membership game with its neighbors. During game play, nodes seek to maximize their local utility by joining or leaving the communities of network neighbors. The community structure emerges after the game reaches a Nash equilibrium. Compared to the benchmark community detection methods, D-GT more accurately predicts the number of communities and finds community assignments with a higher normalized mutual information, while retaining a good modularity.

Accepted in Journal of Social Network Analysis and Mining (SNAM) 2016

References in corpus (7)

• Fast unfolding of communities in large networks
• Modularity and community structure in networks
• Maps of random walks on complex networks reveal community structure
• Near linear time algorithm to detect community structures in large-scale networks
• Resolution limit in community detection
• Community structure in directed networks
• Predicting Positive and Negative Links in Online Social Networks