Personalized PageRank Clustering: A graph clustering algorithm based on random walks

Shayan A. Tabrizi, Azadeh Shakery, Masoud Asadpour, Maziar Abbasi and Mohammad Ali Tavallaie

Physica A: Statistical Mechanics and its Applications, 2013, vol. 392, issue 22, 5772-5785

Abstract: 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.

Keywords: Social networks; Clustering; Community detection; PageRank; Random walks (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:392:y:2013:i:22:p:5772-5785

DOI: 10.1016/j.physa.2013.07.021

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