doi: 10.4304/jsw.6.8.1545-1553
A New Vertex Similarity Metric for Community Discovery: A Local Flow Model
- 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.
Keywords
- hierarchical clustering
- vertex similari
How to Cite
Yueping Li, Yunming Ye, and 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. https://doi.org/ 10.4304/jsw.6.8.1545-1553
Copyright & License
Copyright © 2011 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).