Abstract
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. © 2014 Elsevier Ltd. All rights reserved.
| Original language | English |
|---|---|
| Pages (from-to) | 226-234 |
| Journal | Physica A: Statistical Mechanics and its Applications |
| Volume | 405 |
| Online published | 5 Mar 2014 |
| DOIs | |
| Publication status | Published - 1 Jul 2014 |
Research Keywords
- Community structure
- Online social networks
- Profile information
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