An efficient community detection method based on rank centrality

Yawen Jiang, Caiyan Jia and Jian Yu

Physica A: Statistical Mechanics and its Applications, 2013, vol. 392, issue 9, 2182-2194

Abstract: Community detection is a very important problem in social network analysis. Classical clustering approach, K-means, has been shown to be very efficient to detect communities in networks. However, K-means is quite sensitive to the initial centroids or seeds, especially when it is used to detect communities. To solve this problem, in this study, we propose an efficient algorithm K-rank, which selects the top-K nodes with the highest rank centrality as the initial seeds, and updates these seeds by using an iterative technique like K-means. Then we extend K-rank to partition directed, weighted networks, and to detect overlapping communities. The empirical study on synthetic and real networks show that K-rank is robust and better than the state-of-the-art algorithms including K-means, BGLL, LPA, infomap and OSLOM.

Keywords: Community detection; Clustering; Rank centrality; Vertex similarity; Overlapping communities (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:392:y:2013:i:9:p:2182-2194

DOI: 10.1016/j.physa.2012.12.013

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