A graph clustering method for community detection in complex networks

HongFang Zhou, Jin Li, JunHuai Li, FaCun Zhang and YingAn Cui

Physica A: Statistical Mechanics and its Applications, 2017, vol. 469, issue C, 551-562

Abstract: Information mining from complex networks by identifying communities is an important problem in a number of research fields, including the social sciences, biology, physics and medicine. First, two concepts are introduced, Attracting Degree and Recommending Degree. Second, a graph clustering method, referred to as AR-Cluster, is presented for detecting community structures in complex networks. Third, a novel collaborative similarity measure is adopted to calculate node similarities. In the AR-Cluster method, vertices are grouped together based on calculated similarity under a K-Medoids framework. Extensive experimental results on two real datasets show the effectiveness of AR-Cluster.

Keywords: Complex networks; Collaborative similarity; Graph clustering; Community detection (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:469:y:2017:i:c:p:551-562

DOI: 10.1016/j.physa.2016.11.015

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