Name
Abstract | ||
|---|---|---|
The attribute information of individuals, such as occupation, skill, faith, hobbies and interests, etc, and the structure information amongst individuals, such as mutual relationships between individuals, are two key aspects of information that are used to study individuals and communities in social networks. Considering only the attribute information or the structure relationship alone is insufficient for determining meaningful communities. In this paper, we report an on-going study, we propose an approach that incorporates the structure information of a network and the attribute information of individuals by cooperative games, and game theory is introduced to support strategic decision making in deciding how to recognize communities in social networks, such networks are featured by large number of members, dynamic and with varied ways of connections. This approach provides a model to rationally and logically detect communities in social networks. The Shapley Value in cooperative games is adopted to measure the preference and the contribution of individuals to a specific topic and to the connection closeness of a coalition. We then proposed an iterative formula for computing the Shapley Value to improve the computation efficiency, related theoretical analysis has also been performed. Finally, we further developed an algorithm to detect meaningful communities. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-642-39467-6_24 | BNCOD |
Keywords | Field | DocType |
social network,structure information,meaningful community,shapley value,cooperative game,community detection,on-going study,connection closeness,computation efficiency,structure relationship,game theory,attribute information,social networks | Data science,Data mining,Faith,Strategic decision making,Social network,Computer science,Shapley value,Closeness,Game theory,Management science,Computation | Conference |
Citations | PageRank | References |
5 | 0.46 | 22 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lihua Zhou | 1 | 13 | 1.06 |
| Kevin Lü | 2 | 233 | 18.92 |
| Chao Cheng | 3 | 13 | 1.06 |
| Hongmei Chen | 4 | 25 | 5.39 |