A class of improved algorithms for detecting communities in complex networks

Ju Xiang, Ke Hu and Yi Tang

Physica A: Statistical Mechanics and its Applications, 2008, vol. 387, issue 13, 3327-3334

Abstract: Detecting communities in complex networks is of considerable importance for understanding both the structure and function of the networks. Here, we propose a class of improved algorithms for community detection, by combining the betweenness algorithm of Girvan and Newman with the edge weight defined by the edge-clustering coefficient. The improved algorithms are tested on some artificial and real-world networks, and the results show that they can detect communities of networks more effectively in both unweighted and weighted cases. In addition, the technique for improving the betweenness algorithm in this paper, thanks to its compatibility, can directly be applied to various detection algorithms.

Keywords: Complex network; Community structure; Edge betweenness; Edge-clustering coefficient (search for similar items in EconPapers)
Date: 2008
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:387:y:2008:i:13:p:3327-3334

DOI: 10.1016/j.physa.2008.01.105

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