Volume 6 Number 8 (Aug. 2011)
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JSW 2011 Vol.6(8): 1545-1553 ISSN: 1796-217X
doi: 10.4304/jsw.6.8.1545-1553

A New Vertex Similarity Metric for Community Discovery: A Local Flow Model

Yueping Li, Yunming Ye, Xiaolin Du
Shenzhen Graduate School, Harbin Institute of Technology Shenzhen 518055, China

Abstract—The hierarchical clustering methods based on vertex similarity have the advantage that global evaluation can be incorporated for community discovery. Vertex similarity metric is the most important part of these methods. However, the existing methods do not perform well for community discovery compared with the state-ofthe- art algorithms. In this paper, we propose a new vertex similarity metric based on local flow model, called Local Flow Metric (LFM), for community discovery. LFM considers both the number of connecting paths and the local edge density which are essential measures in community structure. Compared with the existing metrics of vertex similarity, LFM outperforms substantially in community discovery quality and the computing time. Furthermore, our LFM algorithm is superior to the state-of-the-art algorithms in some aspects.

Index Terms—hierarchical clustering, vertex similarity, community discovery, network flow


Cite: Yueping Li, Yunming Ye, Xiaolin Du, "A New Vertex Similarity Metric for Community Discovery: A Local Flow Model," Journal of Software vol. 6, no. 8, pp. 1545-1553, 2011.

General Information

ISSN: 1796-217X (Online)
Frequency:  Bimonthly (Since 2020)
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, Google Scholar, ProQuest, INSPEC(IET), ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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