Title

Community Detection In Dynamic Social Networks: A Game-Theoretic Approach

Keywords

community detection; dynamic social networks; game-theoretic models

Abstract

Most real-world social networks are inherently dynamic and composed of communities that are constantly changing in membership. As a result, recent years have witnessed increased attention toward the challenging problem of detecting evolving communities. This paper presents a game-theoretic approach for community detection in dynamic social networks in which each node is treated as a rational agent who periodically chooses from a set of predefined actions in order to maximize its utility function. The community structure of a snapshot emerges after the game reaches Nash equilibrium; the partitions and agent information are then transferred to the next snapshot. An evaluation of our method on two real world dynamic datasets (AS-Internet Routers Graph and AS-Oregon Graph) demonstrates that we are able to report more stable and accurate communities over time compared to the benchmark methods.

Publication Date

10-10-2014

Publication Title

ASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

Number of Pages

101-107

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/ASONAM.2014.6921567

Socpus ID

84911087193 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84911087193

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