Name
Abstract | ||
|---|---|---|
Local community detection aims at finding a community structure starting from a seed i.e., 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 for local community detection. It estimates the similarity between vertices via the investigation on vertices' neighborhoods, and reveals 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 GMAC algorithm. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-642-40131-2_26 | DaWaK |
Keywords | Field | DocType |
local community detection,seed-insensitive,similarity | Local community,Data mining,Community structure,Data set,Social network,Vertex (geometry),Computer science,Global information,Artificial intelligence,Machine learning | Conference |
Volume | Issue | Citations |
8057 LNCS | null | 18 |
PageRank | References | Authors |
0.78 | 12 | 6 |
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 |
| Jianan Wu | 5 | 22 | 1.86 |
| Yanzhe Che | 6 | 35 | 3.11 |