Name
Abstract | ||
|---|---|---|
The investigation of community structures in networks is an important issue in many domains and disciplines. There have been considerable recent interest algorithms for finding communities in networks. In this paper we present a method of detecting community structure based on hypergraph model. The hypergraph model maps the relationship in the original data into a hypergraph. A hyperedge represents a relationship among subsets of data and the weight of the hyperedge reflects the strength of this affinity. We assign the density of a hyperedge to its weight. We present and illustrate the results of experiments on the Enron data set. These experiments demonstrate that our approach is applicable and effective. |
Year | DOI | Venue |
|---|---|---|
2007 | null | PAISI |
Keywords | Field | DocType |
community structure,scale free,scale free network | Data mining,Community structure,Computer science,Hypergraph,Scale-free network | Conference |
Volume | Issue | ISSN |
4430 LNCS | null | 0302-9743 |
Citations | PageRank | References |
3 | 0.46 | 6 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rong Qian | 1 | 5 | 1.21 |
| Wei Zhang | 2 | 5 | 1.55 |
| Bingru Yang | 3 | 186 | 26.67 |