Name
Abstract | ||
|---|---|---|
Local community detection aims at finding a community structure starting from a seed which is a given vertex in a network without global information, such as online social networks that are too large and dynamic to ever be known fully. Nonetheless, the existing approaches to local community detection are usually sensitive to seeds, i.e., some seeds may lead to missing of some true communities. In this paper, we present a seed-insensitive method called GMAC and its variation iGMAC for local community detection. They estimate the similarity among vertices by investigating vertices' neighborhoods, and reveal a local community by maximizing its internal similarity and minimizing its external similarity simultaneously. Extensive experimental results on both synthetic and real-world data sets verify the effectiveness of our algorithms. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/s10844-014-0315-6 | Journal of Intelligent Information Systems |
Keywords | Field | DocType |
Local community detection,Similarity,Seed-insensitivity | Local community,Data mining,Community structure,Data set,Social network,Vertex (geometry),Computer science,Global information,Artificial intelligence,Machine learning | Journal |
Volume | Issue | ISSN |
43 | 1 | 0925-9902 |
Citations | PageRank | References |
7 | 0.48 | 21 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lianhang Ma | 1 | 58 | 3.96 |
| Hao Huang | 2 | 89 | 7.77 |
| Qinming He | 3 | 371 | 41.53 |
| Kevin Chiew | 4 | 116 | 11.06 |
| Zhenguang Liu | 5 | 47 | 5.09 |