Publication Type

Conference Proceeding Article

Publication Date

8-2016

Abstract

Communities typically capture homophily as people of the same community share many common features. This paper is motivated by the problem of community detection in social networks, as it can help improve our understanding of the network topology. Given the selfish nature of humans to align with like-minded people, we employ game theoretic models and algorithms to detect communities in this paper. Specifically, we employ coordination games to represent interactions between individuals in a social network. We provide a novel and scalable two phased algorithm NashOverlap to compute an accurate overlapping community structure in the given network. We evaluate our algorithm against the best existing methods for community detection and show that our algorithm improves significantly on benchmark networks with respect to standard normalised mutual information measure.

Keywords

Game theory

Discipline

Theory and Algorithms

Research Areas

Intelligent Systems and Decision Analytics

Publication

Proceedings on the 22nd European Conference on Artificial Intelligence: ECAI 2016, The Hague, Netherlands, 2016 August 29 - September 2

Volume

285

First Page

1752

Last Page

1753

ISBN

978-1-61499-671-2

Identifier

10.3233/978-1-61499-672-9-1752

Publisher

IOS Press

City or Country

Amsterdam

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://doi.org./10.3233/978-1-61499-672-9-1752

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