Name
Abstract | ||
|---|---|---|
Community mining in social networks can produce meaningful information, such as activity pattern between individuals and the law of social development. Traditional methods for community identification in static social networks may not find the variation of networks. Besides, a few methods on modeling and analyzing community structures in dynamic social networks fail to identify large networks in acceptable time. Therefore, incremental methods to identify community structures in dynamic social networks are proposed to reduce time complexity. However, some of them merely take network topology into consideration, ignoring a large number of attribute information in real social networks. This paper proposes an incremental method to reveal the actual community structure based on attribute weighted networks. In the method, we associate attribute information with the topological graph. Moreover, time complexity is reduced by setting a threshold which represents a reasonable change rate of edge weight. Experiments on a real-world dataset demonstrate that this approach can reduce time complexity and produce nice community structure. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/CCIS.2014.7175748 | 2014 IEEE 3rd International Conference on Cloud Computing and Intelligence Systems |
Keywords | Field | DocType |
Dynamic social networks,Community mining,Incremental method,Attribute weighting,Change rate of edge weight | Data mining,Community structure,Social network,Community mining,Computer science,Evolving networks,Network topology,Artificial intelligence,Social change,Time complexity,Machine learning,Topological graph | Conference |
ISSN | ISBN | Citations |
2376-5933 | 978-1-4799-4720-1 | 0 |
PageRank | References | Authors |
0.34 | 4 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ying Xia | 1 | 12 | 5.28 |
| linglin tuo | 2 | 0 | 0.34 |