Journal
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
Volume 392, Issue 22, Pages 5772-5785Publisher
ELSEVIER
DOI: 10.1016/j.physa.2013.07.021
Keywords
Social networks; Clustering; Community detection; PageRank; Random walks
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Funding
- ITRC (Research Institute for ICT)
- IPM [CS1390-4-15, CS1390-4-06]
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Graph clustering has been an essential part in many methods and thus its accuracy has a significant effect on many applications. In addition, exponential growth of real-world graphs such as social networks, biological networks and electrical circuits demands clustering algorithms with nearly-linear time and space complexity. In this paper we propose Personalized PageRank Clustering (PPC) that employs the inherent cluster exploratory property of random walks to reveal the clusters of a given graph. We combine random walks and modularity to precisely and efficiently reveal the clusters of a graph. PPC is a top-down algorithm so it can reveal inherent clusters of a graph more accurately than other nearly-linear approaches that are mainly bottom-up. It also gives a hierarchy of clusters that is useful in many applications. PPC has a linear time and space complexity and has been superior to most of the available clustering algorithms on many datasets. Furthermore, its top-down approach makes it a flexible solution for clustering problems with different requirements. (C) 2013 Elsevier B.V. All rights reserved.
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