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