Community detection based on social interactions in a social network

Yen Liang Chen, Ching Hao Chuang, Yu Ting Chiu

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

Abstract

Recent research has involved identifying communities in networks. Traditional methods of community detection usually assume that the network's structural information is fully known, which is not the case in many practical networks. Moreover, most previous community detection algorithms do not differentiate multiple relationships between objects or persons in the real world. In this article, we propose a new approach that utilizes social interaction data (e.g., users' posts on Facebook) to address the community detection problem in Facebook and to find the multiple social groups of a Facebook user. Some advantages to our approach are (a) it does not depend on structural information, (b) it differentiates the various relationships that exist among friends, and (c) it can discover a target user's multiple communities. In the experiment, we detect the community distribution of Facebook users using the proposed method. The experiment shows that our method can achieve the result of having the average scores of Total-Community-Purity and Total-Cluster-Purity both at approximately 0.8.

Original languageEnglish
Pages (from-to)539-550
Number of pages12
JournalJournal of the Association for Information Science and Technology
Volume65
Issue number3
DOIs
StatePublished - Mar 2014

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