Name
Abstract | ||
|---|---|---|
AbstractRecent 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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1002/asi.22986 | Periodicals |
Keywords | Field | DocType |
world wide web,web mining | Social relation,Social group,Data mining,World Wide Web,Web mining,Social network,Computer science | Journal |
Volume | Issue | ISSN |
65 | 3 | 2330-1635 |
Citations | PageRank | References |
7 | 0.47 | 12 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yen-Liang Chen | 1 | 1361 | 73.85 |
| Ching-Hao Chuang | 2 | 49 | 3.95 |
| Yu-Ting Chiu | 3 | 55 | 8.52 |