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Local Graph Clustering by Multi-network Random Walk with Restart

  • Yaowei YanEmail author
  • Dongsheng Luo
  • Jingchao Ni
  • Hongliang Fei
  • Wei Fan
  • Xiong Yu
  • John Yen
  • Xiang Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10939)

Abstract

Searching local graph clusters is an important problem in big network analysis. Given a query node in a graph, local clustering aims at finding a subgraph around the query node, which consists of nodes highly relevant to the query node. Existing local clustering methods are based on single networks that contain limited information. In contrast, the real data are always comprehensive and can be represented better by multiple connected networks (multi-network). To take the advantage of heterogeneity of multi-network and improve the clustering accuracy, we advance a strategy for local graph clustering based on Multi-network Random Walk with Restart (MRWR), which discovers local clusters on a target network in association with additional networks. For the proposed local clustering method, we develop a localized approximate algorithm (AMRWR) on solid theoretical basis to speed up the searching process. To the best of our knowledge, this is the first elaboration of local clustering on a target network by integrating multiple networks. Empirical evaluations show that the proposed method improves clustering accuracy by more than 10% on average with competently short running time, compared with the alternative state-of-the-art graph clustering approaches.

Notes

Acknowledgement

This work was partially supported by the National Science Foundation grants IIS-1664629, SES-1638320, CAREER, and the National Institute of Health grant R01GM115833. We also thank the anonymous reviewers for their valuable comments and suggestions.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Yaowei Yan
    • 1
    Email author
  • Dongsheng Luo
    • 1
  • Jingchao Ni
    • 1
  • Hongliang Fei
    • 2
  • Wei Fan
    • 3
  • Xiong Yu
    • 4
  • John Yen
    • 1
  • Xiang Zhang
    • 1
  1. 1.College of Information Sciences and TechnologyThe Pennsylvania State UniversityState CollegeUSA
  2. 2.Baidu Research Big Data LabSunnyvaleUSA
  3. 3.Tencent Medical AI LabPalo AltoUSA
  4. 4.Case Western Reserve UniversityClevelandUSA

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