Article ID Journal Published Year Pages File Type
977346 Physica A: Statistical Mechanics and its Applications 2014 9 Pages PDF
Abstract

•We construct additional networks with users’ profile information.•We propose methods to improve values of modularity.•With our methods, partitions are more correlated with users’ characteristic features.

Community structure is an important feature in the study of complex networks. It is because nodes of the same community may have similar properties. In this paper we extend two popular community detection methods to partition online social networks. In our extended methods, the profile information of users is used for partitioning. We apply the extended methods in several sample networks of Facebook. Compared with the original methods, the community structures we obtain have higher modularity. Our results indicate that users’ profile information is consistent with the community structure of their friendship network to some extent. To the best of our knowledge, this paper is the first to discuss how profile information can be used to improve community detection in online social networks.

Related Topics
Physical Sciences and Engineering Mathematics Mathematical Physics
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