Name
Abstract | ||
|---|---|---|
Modularity maximization is an effective technique for identifying communities in networks that exhibit a natural division into tightly connected groups of vertices. However, not all networks possess a strong enough community structure to justify the use of modularity maximization. We introduce the concept of base clusters-that is group of vertices that form the kernel of each community and are always assigned together independent of the community detection algorithm used or the permutation of the vertices. If the number of vertices in the base clusters is high then the network is likely to have distinct communities and is suitable for the modularity maximization approach. We develop an algorithm for obtaining these base clusters and show that identifying base clusters as a preprocessing step can help in improving the modularity values for agglomerative methods. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1090/conm/588/11708 | GRAPH PARTITIONING AND GRAPH CLUSTERING |
Keywords | Field | DocType |
Modularity maximization, agglomerative methods, communities in complex networks | Cluster (physics),Computer science,Theoretical computer science,Modularity | Conference |
Volume | ISSN | Citations |
588 | 0271-4132 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sriram Srinivasan | 1 | 379 | 27.92 |
| Tanmoy Chakraborty | 2 | 466 | 76.71 |
| Sanjukta Bhowmick | 3 | 120 | 18.83 |