An important challenge in big data analysis nowadays is detection of cohesivegroups in large-scale networks, including social networks, genetic networks,communication networks and so. In this paper, we propose LabelRank, anefficient algorithm detecting communities through label propagation. A set ofoperators is introduced to control and stabilize the propagation dynamics.These operations resolve the randomness issue in traditional label propagationalgorithms (LPA), stabilizing the discovered communities in all runs of thesame network. Tests on real-world networks demonstrate that LabelRanksignificantly improves the quality of detected communities compared to LPA, aswell as other popular algorithms.
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