Name
Abstract | ||
|---|---|---|
The advances in social networks has led to the concentration of research on analyzing people's behaviors in these networks. Accordingly, detecting communities and the interactions between their members is one of the most important issues addressed by these studies. After the proposition of new community detection methods in recent years, due to the extensive volume of the information generated in social networks and the increasing growth in the size of these networks, researchers became more interested in local, rather than global, detection methods. This paper proposes a heuristic approach to detecting communities by investigating local information. Comparing this method with state-of-the-art approaches, it is observed that the proposed approach outperforms the compared methods in detecting communities and their members and provides more accurate results. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/s10489-016-0824-9 | Appl. Intell. |
Keywords | Field | DocType |
Local community detection,Social networks | Local community,Heuristic,Proposition,Social network,Computer science,Artificial intelligence,Machine learning | Journal |
Volume | Issue | ISSN |
46 | 1 | 0924-669X |
Citations | PageRank | References |
9 | 0.49 | 18 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mohammad Ali Tabarzad | 1 | 9 | 0.83 |
| Ali Hamzeh | 2 | 214 | 29.47 |