Name
Abstract | ||
|---|---|---|
Finding decompositions of a graph into a family of clusters is crucial to understanding its underlying structure. While most existing approaches focus on partitioning the nodes, real-world datasets suggest the presence of overlapping communities. We present OCA, a novel algorithm to detect overlapped communities in large data graphs. It outperforms previous proposals in terms of execution time, and efficiently handles large graphs containing more than 108 nodes and edges. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ICDE.2010.5447860 | Data Engineering |
Keywords | Field | DocType |
graph theory,OCA,community search overlapping,data graphs,graph decompositions,real world datasets,social networks | Graph theory,Community search,Data mining,Cluster (physics),Graph,Social network,Computer science,Theoretical computer science,Execution time,Encyclopedia,Database,The Internet | Conference |
ISSN | ISBN | Citations |
1084-4627 | 978-1-4244-5444-0 | 6 |
PageRank | References | Authors |
0.69 | 1 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Arnau Padrol-Sureda | 1 | 6 | 0.69 |
| Guillem Perarnau-Llobet | 2 | 6 | 0.69 |
| Julian Pfeifle | 3 | 31 | 6.56 |
| Victor Muntés-Mulero | 4 | 204 | 22.79 |
| Padrol-Sureda, A. | 5 | 6 | 0.69 |
| Perarnau-Llobet, G. | 6 | 6 | 0.69 |
| Muntes-Mulero, V. | 7 | 6 | 0.69 |