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Local Community Detection Algorithm Based on Minimal Cluster
Joint Authors
Wang, Zhixiao
Xing, Yan
Sun, Guibin
Zhou, Ranran
Yong, Zhou
Source
Applied Computational Intelligence and Soft Computing
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-11-07
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Information Technology and Computer Science
Abstract EN
In order to discover the structure of local community more effectively, this paper puts forward a new local community detection algorithm based on minimal cluster.
Most of the local community detection algorithms begin from one node.
The agglomeration ability of a single node must be less than multiple nodes, so the beginning of the community extension of the algorithm in this paper is no longer from the initial node only but from a node cluster containing this initial node and nodes in the cluster are relatively densely connected with each other.
The algorithm mainly includes two phases.
First it detects the minimal cluster and then finds the local community extended from the minimal cluster.
Experimental results show that the quality of the local community detected by our algorithm is much better than other algorithms no matter in real networks or in simulated networks.
American Psychological Association (APA)
Yong, Zhou& Sun, Guibin& Xing, Yan& Zhou, Ranran& Wang, Zhixiao. 2016. Local Community Detection Algorithm Based on Minimal Cluster. Applied Computational Intelligence and Soft Computing،Vol. 2016, no. 2016, pp.1-11.
https://search.emarefa.net/detail/BIM-1094897
Modern Language Association (MLA)
Yong, Zhou…[et al.]. Local Community Detection Algorithm Based on Minimal Cluster. Applied Computational Intelligence and Soft Computing No. 2016 (2016), pp.1-11.
https://search.emarefa.net/detail/BIM-1094897
American Medical Association (AMA)
Yong, Zhou& Sun, Guibin& Xing, Yan& Zhou, Ranran& Wang, Zhixiao. Local Community Detection Algorithm Based on Minimal Cluster. Applied Computational Intelligence and Soft Computing. 2016. Vol. 2016, no. 2016, pp.1-11.
https://search.emarefa.net/detail/BIM-1094897
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1094897