Name
Abstract | ||
|---|---|---|
Evolutionary community discovery is a hot research topic which clusters the dynamic or temporal network. The communities detected in dynamic network should get reasonable partition for the current data while simultaneously not deviate drastically from the previous ones. In this paper, the evolutionary community discovery algorithm based on leader nodes (EvoLeaders) is proposed to cluster the dynamic network. Compared with the static community discovery algorithm based on leader nodes (the Top Leaders algorithm), experimental results over two real-world datasets demonstrate that the EvoLeaders is more suitable for dynamic scenarios. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/BIGCOMP.2016.7425801 | 2016 International Conference on Big Data and Smart Computing (BigComp) |
Keywords | Field | DocType |
dynamic network,community discovery,leader nodes | Leader election,Dynamic network analysis,Cluster (physics),Data mining,Community structure,Computer science,Merge (version control),Cluster analysis | Conference |
ISSN | Citations | PageRank |
2375-933X | 6 | 0.43 |
References | Authors | |
11 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wenhao Gao | 1 | 6 | 0.43 |
| Wenjian Luo | 2 | 356 | 40.95 |
| Chenyang Bu | 3 | 47 | 9.18 |