Name
Abstract | ||
|---|---|---|
Finding communities in evolving networks is a difficult task and raises issues different from the classic static detection case. We introduce an approach based on the recent vertex-centred paradigm. The proposed algorithm, named DynLOCNeSs, detects communities by scanning and evaluating each vertex neighbourhood by means of a preference measure, using these preferences to handle community changes. We also introduce a new vertex neighbourhood preference measure, CWCN, more efficient than current existing ones in the considered context. Experimental results show the relevance of this measure and the ability of the proposed approach to detect classical community evolution patterns such as grow-shrink and merge-split. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-50901-3_22 | Studies in Computational Intelligence |
DocType | Volume | ISSN |
Conference | 693 | 1860-949X |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Maël Canu | 1 | 0 | 0.68 |
| Marie-Jeanne Lesot | 2 | 220 | 32.41 |
| Adrien Revault | 3 | 12 | 4.72 |