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Social Network Analysis and Mining
Published in:

01-12-2016 | Original Article

Identifying community structures in dynamic networks

Authors: Hamidreza Alvari, Alireza Hajibagheri, Gita Sukthankar, Kiran Lakkaraju

Published in: Social Network Analysis and Mining | Issue 1/2016

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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.
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Footnotes
1
We employ the notation for directed graphs, although it is straightforward to generalize to undirected graphs by ignoring the in/out superscripts.
 
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Metadata
Title
Identifying community structures in dynamic networks
Authors
Hamidreza Alvari
Alireza Hajibagheri
Gita Sukthankar
Kiran Lakkaraju
Publication date
01-12-2016
Publisher
Springer Vienna
Published in
Social Network Analysis and Mining / Issue 1/2016
Print ISSN: 1869-5450
Electronic ISSN: 1869-5469
DOI
https://doi.org/10.1007/s13278-016-0390-5

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