DocumentCode :
3040892
Title :
An Efficient Algorithm for Overlapping Community Detection in Complex Networks
Author :
Chen, Duanbing ; Fu, Yan ; Shang, Mingsheng
Author_Institution :
Sch. of Comput. Sci., Univ. of Electron. Sci. & Technol. of China, Chengdu, China
Volume :
1
fYear :
2009
fDate :
19-21 May 2009
Firstpage :
244
Lastpage :
247
Abstract :
Community structure is an important property of the complex networks. How to detect the communities is significant to understand the network structure and analyze the network properties. Many algorithms, such as K-L and GN, have been proposed to detect the community structure in complex networks. However, the communities detected by these algorithms are always not overlapping. According to daily experience, a node in the network may belong to several communities and a community should have many nodes and connections. Based on these principals and existing researches, an efficient algorithm for overlapping community detection in complex networks is proposed in this paper. The key strategy of the algorithm is to mine a node with the closest relations with the community and assign it to the community. Some real-world networks are used to test the performance of the algorithm. Experimental results demonstrate that the algorithm proposed is rather efficient to detect the overlapping community in complex networks.
Keywords :
complex networks; network theory (graphs); community detection; community structure; complex network; efficient algorithm; network properties; network structure; Clustering algorithms; Complex networks; Computer science; Intelligent networks; Intelligent structures; Intelligent systems; Laplace equations; Partitioning algorithms; Sparse matrices; Testing; algorithm; complex networks; detection; overlapping community;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Systems, 2009. GCIS '09. WRI Global Congress on
Conference_Location :
Xiamen
Print_ISBN :
978-0-7695-3571-5
Type :
conf
DOI :
10.1109/GCIS.2009.68
Filename :
5208980
Link To Document :
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