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

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

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.

Suggested Citation

  • A. Tabrizi, Shayan & Shakery, Azadeh & Asadpour, Masoud & Abbasi, Maziar & Tavallaie, Mohammad Ali, 2013. "Personalized PageRank Clustering: A graph clustering algorithm based on random walks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(22), pages 5772-5785.
    • Handle: RePEc:eee:phsmap:v:392:y:2013:i:22:p:5772-5785
    DOI: 10.1016/j.physa.2013.07.021
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    References listed on IDEAS

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    Cited by:

    1. Mei Chen & Zhichong Yang & Xiaofang Wen & Mingwei Leng & Mei Zhang & Ming Li, 2019. "Effectively Detecting Communities by Adjusting Initial Structure via Cores," Complexity, Hindawi, vol. 2019, pages 1-20, November.
    2. Jianjun Cheng & Xing Su & Haijuan Yang & Longjie Li & Jingming Zhang & Shiyan Zhao & Xiaoyun Chen, 2019. "Neighbor Similarity Based Agglomerative Method for Community Detection in Networks," Complexity, Hindawi, vol. 2019, pages 1-16, May.
    3. Fuentes, Emilio Aced & Santini, Simone, 2021. "Network navigation with non-Lèvy superdiffusive random walks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 580(C).

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