Name
Abstract | ||
|---|---|---|
Community detection is an important technique to understand the structure of complex social networks. Many approaches have been devised to extract community structures in recent years. In this paper we propose a novel neighborhood vector propagation algorithm (NVPA) to detect communities in a social network which has greater accuracy than algorithms in the literature. In our approach, a neighborhood vector is proposed to store the neighborhood information, and a vector propagation algorithm is designed to disseminate neighborhood information to other nodes. After neighborhood propagation, hierarchical clustering is used to find the community structure based on similarity measures. We apply our algorithm on two real-world networks and LRF benchmark networks. Experimental results show that our algorithm achieves greater accuracy than several well known algorithms in the literature. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/GLOCOM.2014.7037252 | GLOBECOM |
Keywords | Field | DocType |
pattern clustering,lrf benchmark networks,neighborhood propagation,complex social networks,neighborhood vector propagation algorithm,similarity measures,community structures,nvpa,neighborhood information,social networking (online),community detection,hierarchical clustering,vectors,accuracy,clustering algorithms,algorithm design and analysis | Hierarchical clustering,Data mining,Community structure,Algorithm design,Social network,Computer science,Algorithm,Dissemination,Artificial intelligence,FLAME clustering,Cluster analysis,Machine learning | Conference |
ISSN | Citations | PageRank |
2334-0983 | 3 | 0.44 |
References | Authors | |
2 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiao Liang | 1 | 3 | 0.77 |
| Junhua Tang | 2 | 63 | 12.59 |
| Li Pan | 3 | 61 | 6.95 |