Name
Abstract | ||
|---|---|---|
The community detection in social networks is important to understand the structural and functional properties of networks. In this paper we propose a coalitional game model for community detection in social networks, and use the Shapley Value in coalitional games to evaluate each individual's contribution to the closeness of connection. We then develop an iterative formula for computing the Shapley Value to improve the computation efficiency. We further propose a hierarchical clustering algorithm GAMEHC to detect communities in social networks. The effectiveness of our methods is verified by preliminary experimental result. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-642-38562-9_33 | WAIM |
Keywords | Field | DocType |
social network,coalitional game,preliminary experimental result,iterative formula,functional property,coalitional game model,shapley value,community detection,computation efficiency,hierarchical clustering algorithm gamehc | Hierarchical clustering,Social network,Computer science,Closeness,Simulation,Shapley value,Theoretical computer science,Artificial intelligence,Game theory,Machine learning,Computation | Conference |
Citations | PageRank | References |
8 | 0.61 | 11 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lihua Zhou | 1 | 13 | 1.06 |
| Chao Cheng | 2 | 13 | 1.06 |
| Kevin Lü | 3 | 233 | 18.92 |
| Hongmei Chen | 4 | 25 | 5.39 |