Identifying Community Structures in Dynamic Networks

TL;DR

This paper introduces D-GT, a game theoretic method for detecting evolving communities in dynamic networks, which models nodes as rational agents seeking optimal community memberships, outperforming benchmarks in accuracy.

Contribution

The paper presents a novel dynamic community detection approach using game theory, modeling nodes as rational agents to improve accuracy in identifying evolving communities.

Findings

D-GT predicts the number of communities more accurately.

D-GT achieves higher normalized mutual information.

D-GT maintains good modularity.

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.