Identifying Community Structures In Dynamic Networks

Keywords

Community detection; Dynamic social networks; Game-theoretic models

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.

Publication Date

12-1-2016

Publication Title

Social Network Analysis and Mining

Volume

6

Issue

1

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1007/s13278-016-0390-5

Socpus ID

84987850151 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84987850151

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