Name
Abstract | ||
|---|---|---|
With the growing number of available social and biological networks, the problem of detecting network community structure is becoming more and more important which acts as the first step to analyze these data. In this paper, we transform network data so that each node is represented by a vector, our method can handle directed and weighted networks. it also can detect networks which contain communities with different sizes and degree sequences. This paper reveals that network community can be formulated as a cluster problem. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-540-69733-6_25 | COCOON |
Keywords | Field | DocType |
detecting community structure,degree sequence,weighted network,biological network,network community structure,network community,network data,cluster problem,network vectorization,different size,community structure | Dynamic network analysis,Data mining,Community structure,Computer science,Biological network,Network simulation,Vectorization (mathematics),Network data,Weighted correlation network analysis,Latent semantic analysis | Conference |
Volume | ISSN | Citations |
5092 | 0302-9743 | 1 |
PageRank | References | Authors |
0.37 | 4 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wei Ren | 1 | 1 | 0.71 |
| Guiying Yan | 2 | 196 | 22.92 |
| Guohui Lin | 3 | 1301 | 107.34 |
| Caifeng Du | 4 | 1 | 0.37 |
| Xiaofeng Han | 5 | 5 | 2.63 |