Title :
A neighborhood vector propagation algorithm for community detection
Author :
Xiao Liang ; Junhua Tang ; Li Pan
Author_Institution :
Sch. of Electron. Inf. & Electr. Eng., Shanghai Jiao Tong Univ., Shanghai, China
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.
Keywords :
pattern clustering; social networking (online); vectors; LRF benchmark networks; NVPA; community detection; community structures; complex social networks; hierarchical clustering; neighborhood information; neighborhood propagation; neighborhood vector propagation algorithm; similarity measures; Accuracy; Algorithm design and analysis; Clustering algorithms; Communities; Complexity theory; Social network services; Vectors;
Conference_Titel :
Global Communications Conference (GLOBECOM), 2014 IEEE
Conference_Location :
Austin, TX
DOI :
10.1109/GLOCOM.2014.7037252