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)


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.



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.


  1. 1.
    Ni, J., Fei, H., Fan, W., Zhang, X.: Cross-network clustering and cluster ranking for medical diagnosis. In: ICDE (2017)Google Scholar
  2. 2.
    Ni, J., Koyuturk, M., Tong, H., Haines, J., Rong, X., Zhang, X.: Disease gene prioritization by integrating tissue-specific molecular networks using a robust multi-network model. BMC Bioinform. 17(1), 453 (2016)CrossRefGoogle Scholar
  3. 3.
    Liu, R., Cheng, W., Tong, H., Wang, W., Zhang, X.: Robust multi-network clustering via joint cross-domain cluster alignment. In: ICDM (2015)Google Scholar
  4. 4.
    Sozio, M., Gionis, A.: The community-search problem and how to plan a successful cocktail party. In: KDD (2010)Google Scholar
  5. 5.
    Schaeer, S.E.: Graph clustering. Comput. Sci. Rev. 1(1), 27–64 (2007)CrossRefGoogle Scholar
  6. 6.
    Yubao, W., Jin, R., Li, J., Zhang, X.: Robust local community detection: on free rider effect and its elimination. Proc. VLDB Endow. 8(7), 798–809 (2015)CrossRefGoogle Scholar
  7. 7.
    Kloumann, I.M., Kleinberg, J.M.: Community membership identification from small seed sets. In: KDD (2014)Google Scholar
  8. 8.
    Cui, W., Xiao, Y., Wang, H., Wang, W.: Local search of communities in large graphs. In: SIGMOD (2014)Google Scholar
  9. 9.
    Kloster, K., Gleich, D.F.: Heat kernel based community detection. In: SIGKDD (2014)Google Scholar
  10. 10.
    Andersen, R., Chung, F., Lang, K.: Local graph partitioning using pagerank vectors. In: FOCS (2006)Google Scholar
  11. 11.
    Zhou, D., Burges, C.J.C: Spectral clustering and transductive learning with multiple views. In: ICML (2007)Google Scholar
  12. 12.
    Kumar, A., Rai, P., Daume, H.: Co-regularized multi-view spectral clustering. In: Advances in neural information processing systems (2011)Google Scholar
  13. 13.
    Kumar, A., Daumé, H.: A co-training approach for multi-view spectral clustering. In: ICML (2011)Google Scholar
  14. 14.
    Cheng, W., Zhang, X., Guo, Z., Yubao, W., Sullivan, P.F., Wang, W.: Flexible and robust co-regularized multi-domain graph clustering. In: KDD (2013)Google Scholar
  15. 15.
    Ni, J., Tong, H., Fan, W., Zhang, X.: Flexible and robust multi-network clustering. In: KDD (2015)Google Scholar
  16. 16.
    Yubao, W., Bian, Y., Zhang, X.: Remember where you came from: on the second-order random walk based proximity measures. Proc. VLDB Endow. 10(1), 13–24 (2016)CrossRefGoogle Scholar
  17. 17.
    Schaeffer, S.E.: Stochastic local clustering for massive graphs. In: Ho, T.B., Cheung, D., Liu, H. (eds.) PAKDD 2005. LNCS (LNAI), vol. 3518, pp. 354–360. Springer, Heidelberg (2005). Scholar
  18. 18.
    Huang, X., Cheng, H., Qin, L., Tian, W., Yu, J.X.: Querying k-truss community in large and dynamic graphs. In: SIGMOD (2014)Google Scholar
  19. 19.
    Martins, P.: Modeling the maximum edge-weight k-plex partitioning problem (2016). arXiv preprint arXiv:1612.06243
  20. 20.
    Tong, H., Faloutsos, C., Gallagher, B., Eliassi-Rad, T.: Fast best-effort pattern matching in large attributed graphs. In: KDD (2007)Google Scholar
  21. 21.
    Tong, H., Faloutsos, C., Pan, J.Y.: Fast random walk with restart and its applications (2006)Google Scholar
  22. 22.
    Yan, Y., et al.: Local Graph Clustering by Multi-network Random Walk with Restart, Technical report.
  23. 23.
    Van Driel, M.A., Bruggeman, J., Vriend, G., Brunner, H.G., Leunissen, J.A.M.: A text-mining analysis of the human phenome. Eur. J. Hum. Genet. 14(5), 535–542 (2006)CrossRefGoogle Scholar
  24. 24.
    Ji, M., Sun, Y., Danilevsky, M., Han, J., Gao, J.: Graph regularized transductive classification on heterogeneous information networks. In: Balcázar, J.L., Bonchi, F., Gionis, A., Sebag, M. (eds.) ECML PKDD 2010. LNCS (LNAI), vol. 6321, pp. 570–586. Springer, Heidelberg (2010). Scholar
  25. 25.
    Fang, Y., Cheng, R., Luo, S., Jiafeng, H.: Effective community search for large attributed graphs. Proc. VLDB Endow. 9(12), 1233–1244 (2016)CrossRefGoogle Scholar
  26. 26.
    Perozzi, B., Akoglu, L., Iglesias Sánchez, P., Müller, E.: Focused clustering and outlier detection in large attributed graphs. In: KDD (2014)Google Scholar

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

Personalised recommendations