Name
Abstract | ||
|---|---|---|
In this paper, we propose an algorithm that detects overlapping communities in networks (graphs) based on a simple node behavior model. The key idea in the proposed algorithm is to find communities in an agglomerative manner such that every detected community S has the following property: For each node i ∈ S, we have (i) the fraction of nodes in S {i} that are neighbors of node i is greater than a given threshold, or (ii) the fraction of neighbors of node i that are in S {i} is greater than another given threshold. Through computer simulations of random graphs with built-in overlapping community structure, including LFR benchmark random graphs and Erdös-Rényi type random graphs, we show that our algorithm has excellent performance. Furthermore, we apply our algorithm to several real-world networks and show that the overlapping communities detected by our algorithm are very close to the known communities in these networks. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/GLOCOM.2013.6831551 | GLOBECOM |
Keywords | Field | DocType |
erdös-rényi type random graphs,clustering algorithms,social networks,lfr benchmark random graphs,node behavior model,overlapping communities,large complex networks,social networking (online),overlapping communities detection,computer simulations,benchmark testing,simulation | Hierarchical clustering,Graph,Community structure,Random graph,Computer science,Theoretical computer science,Complex network,Benchmark (computing) | Conference |
ISSN | Citations | PageRank |
2334-0983 | 0 | 0.34 |
References | Authors | |
5 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xuan-Chao Huang | 1 | 6 | 1.87 |
| Jay Cheng | 2 | 153 | 14.40 |
| Hsin-Hung Chou | 3 | 54 | 5.37 |
| Chih-Heng Cheng | 4 | 0 | 1.35 |
| Hsien-Tsan Chen | 5 | 0 | 0.34 |