Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/138072
Title: Community detection using coordination games
Authors: Arava, Radhika
Keywords: Science::Mathematics
Issue Date: 2018
Source: Arava, R. (2018). Community detection using coordination games. Social Network Analysis and Mining, 8(1), 65-. doi:10.1007/s13278-018-0543-9
Journal: Social Network Analysis and Mining
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 and the spread of information. 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 represent the problem of community detection as a graph coordination game. We provide a novel and scalable two-phased probabilistic semi-supervised approach 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 (real and synthetic) with respect to standard normalized mutual information measure.
URI: https://hdl.handle.net/10356/138072
ISSN: 1869-5450
DOI: 10.1007/s13278-018-0543-9
Schools: School of Physical and Mathematical Sciences 
Rights: This is a post-peer-review, pre-copyedit version of an article published in Social Network Analysis and Mining. The final authenticated version is available online at: http://dx.doi.org/10.1007/s13278-018-0543-9
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SPMS Journal Articles

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