Name
Abstract | ||
|---|---|---|
Local community detection refers to finding the community that contains the given node based on local information, which becomes very meaningful when global information about the network is unavailable or expensive to acquire. Most studies on local community detection focus on finding non-overlapping communities. However, many real-world networks contain overlapping communities like social networks. Given an overlapping node that belongs to multiple communities, the problem is to find communities to which it belongs according to local information. We propose a framework for local overlapping community detection. The framework has three steps. First, find nodes in multiple communities to which the given node belongs. Second, select representative nodes from nodes obtained above, which tends to be in different communities. Third, discover the communities to which these representative nodes belong. In addition, to demonstrate the effectiveness of the framework, we implement six versions of this framework. Experimental results demonstrate that the six implementation versions outperform the other algorithms.
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Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3361739 | ACM Transactions on Knowledge Discovery from Data (TKDD) |
Keywords | Field | DocType |
Social network, community detection, local community detection, local overlapping community detection | Pattern recognition,Computer science,Artificial intelligence | Journal |
Volume | Issue | ISSN |
14 | 1 | 1556-4681 |
Citations | PageRank | References |
2 | 0.36 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Li Ni | 1 | 8 | 4.18 |
| Li Ni | 2 | 8 | 4.18 |
| Wenjie Zhu | 3 | 3 | 1.40 |
| Bei Hua | 4 | 146 | 17.17 |