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Data Mining and Knowledge Discovery

, Volume 31, Issue 5, pp 1506–1543 | Cite as

Ensemble-based community detection in multilayer networks

  • Andrea Tagarelli
  • Alessia Amelio
  • Francesco Gullo
Article
Part of the following topical collections:
  1. Journal Track of ECML PKDD 2017

Abstract

The problem of community detection in a multilayer network can effectively be addressed by aggregating the community structures separately generated for each network layer, in order to infer a consensus solution for the input network. To this purpose, clustering ensemble methods developed in the data clustering field are naturally of great support. Bringing these methods into a community detection framework would in principle represent a powerful and versatile approach to reach more stable and reliable community structures. Surprisingly, research on consensus community detection is still in its infancy. In this paper, we propose a novel modularity-driven ensemble-based approach to multilayer community detection. A key aspect is that it finds consensus community structures that not only capture prototypical community memberships of nodes, but also preserve the multilayer topology information and optimize the edge connectivity in the consensus via modularity analysis. Empirical evidence obtained on seven real-world multilayer networks sheds light on the effectiveness and efficiency of our proposed modularity-driven ensemble-based approach, which has shown to outperform state-of-the-art multilayer methods in terms of modularity, silhouette of community memberships, and redundancy assessment criteria, and also in terms of execution times.

Keywords

Community detection Ensemble clustering Consensus clustering Multilayer networks Modularity optimization 

References

  1. Amelio A, Pizzuti C (2014a) A cooperative evolutionary approach to learn communities in multilayer networks. In: Proceedings of PSSN, pp 222–232Google Scholar
  2. Amelio A, Pizzuti C (2014b) Community detection in multidimensional networks. In: Proceedings of ICTAI, pp 352–359Google Scholar
  3. Berlingerio M, Coscia M, Giannotti F (2011) Finding and characterizing communities in multidimensional networks. In: Proceedings of ASONAM, pp 490–494Google Scholar
  4. Berlingerio M, Pinelli F, Calabrese F (2013) ABACUS: frequent pattern mining-based community discovery in multidimensional networks. Data Min Knowl Discov 27(3):294–320MathSciNetCrossRefzbMATHGoogle Scholar
  5. Burgess M, Adar E, Cafarella M (2016) Link-prediction enhanced consensus clustering for complex networks. PLoS ONE 11(5):e0153384CrossRefGoogle Scholar
  6. De Domenico M, Lancichinetti A, Arenas A, Rosvall M (2015) Identifying modular flows on multilayer networks reveals highly overlapping organization in interconnected systems. Phys Rev X 5:011027Google Scholar
  7. Dhillon IS, Guan Y, Kulis B (2004) Kernel k-means: spectral clustering and normalized cuts. In: Proceedings of ACM KDD, pp 551–556Google Scholar
  8. Dickison ME, Magnani M, Rossi L (2016) Multilayer social networks. Cambridge University Press, UKCrossRefGoogle Scholar
  9. Dong X, Frossard P, Vandergheynst P, Nefedov N (2012) Clustering with multi-layer graphs: a spectral perspective. IEEE Trans Signal Process 60(11):5820–5831MathSciNetCrossRefGoogle Scholar
  10. Fortunato S, Barthelemy M (2007) Resolution limit in community detection. Proc Natl Acad Sci 104:36–41CrossRefGoogle Scholar
  11. Fred A (2001) Finding consistent clusters in data partitions. In: Proceedings of work. on multiple classifier systems, pp 309–318Google Scholar
  12. Gullo F, Tagarelli A, Greco S (2009) Diversity-based weighting schemes for clustering ensembles. In: Proceedings of SDM, pp 437–448Google Scholar
  13. Hmimida M, Kanawati R (2015) Community detection in multiplex networks: a seed-centric approach. Netw Heterog Media 10(1):71–85MathSciNetCrossRefzbMATHGoogle Scholar
  14. Katselis D, Beck CL, van der Schaar M (2014) Ensemble online clustering through decentralized observations. In: Proceedings of IEEE conference on decision and control, pp 910–915Google Scholar
  15. Khan I, Huang JZ, Ivanov K (2016) Incremental density-based ensemble clustering over evolving data streams. Neurocomputing 191:34–43CrossRefGoogle Scholar
  16. Kim J, Lee J-G (2015) Community detection in multi-layer graphs: a survey. SIGMOD Rec 44(3):37–48CrossRefGoogle Scholar
  17. Kivela M, Arenas A, Barthelemy M, Gleeson JP, Moreno Y, Porter MA (2014) Multilayer networks. J Complex Netw 2(3):203–271CrossRefGoogle Scholar
  18. Kuncheva Z, Montana G (2015) Community detection in multiplex networks using locally adaptive random walks. In: Proceedings of ASONAM, pp 1308–1315Google Scholar
  19. Lancichinetti A, Fortunato S (2012) Consensus clustering in complex networks. Sci Rep 2:336CrossRefGoogle Scholar
  20. Lancichinetti A, Fortunato S, Radicchi F (2008) Benchmark graphs for testing community detection algorithms. Phys Rev E 78(4):046110CrossRefGoogle Scholar
  21. LaSalle D, Karypis G (2015) Multi-threaded modularity based graph clustering using the multilevel paradigm. J Parallel Distrib Comput 76:66–80CrossRefGoogle Scholar
  22. Li T, Ding C, Jordan MI (Oct 2007) Solving consensus and semi-supervised clustering problems using nonnegative matrix factorization. In: Proceedings of ICDM, pp 577–582Google Scholar
  23. Mucha PJ, Richardson T, Macon K, Porter MA, Onnela J-P (2010) Community structure in time-dependent, multiscale, and multiplex networks. Science 328(5980):876–878MathSciNetCrossRefzbMATHGoogle Scholar
  24. Newman MEJ, Girvan M (2004) Finding and evaluating community structure in networks. Phys Rev E 69(2):026113CrossRefGoogle Scholar
  25. Nguyen N, Caruana R (2007) Consensus clusterings. In: Proceedings of ICDM, pp 607–612Google Scholar
  26. Rocklin M, Pinar A (2013) On clustering on graphs with multiple edge types. Internet Math 9(1):82–112MathSciNetCrossRefzbMATHGoogle Scholar
  27. Rosvall M, Bergstrom CT (2008) Maps of random walks on complex networks reveal community structure. Proc Natl Acad Sci USA 105:1118–1123CrossRefGoogle Scholar
  28. Rousseeuw PJ (1987) Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math 20:53–65CrossRefzbMATHGoogle Scholar
  29. Strehl A, Ghosh J (2003) Cluster ensembles: a knowledge reuse framework for combining multiple partitions. J Mach Learn Res 3:583–617MathSciNetzbMATHGoogle Scholar
  30. Tang L, Wang X, Liu H (2009) Uncovering groups via heterogeneous interaction analysis. In: Proceedings of ICDM, pp 503–512Google Scholar
  31. Tang L, Wang X, Liu H (2012) Community detection via heterogeneous interaction analysis. Data Min Knowl Discov 25:1–33MathSciNetCrossRefGoogle Scholar
  32. Yu Z, Luo P, You J, Wong H-S, Leung H, Wu S, Zhang J, Han G (2016) Incremental semi-supervised clustering ensemble for high dimensional data clustering. IEEE Trans Knowl Data Eng 28(3):701–714CrossRefGoogle Scholar
  33. Zhang H, Wang C-D, Lai J-H, Yu PS (2017) Modularity in complex multilayer networks with multiple aspects: a static perspective. Appl Inform 4:7CrossRefGoogle Scholar

Copyright information

© The Author(s) 2017

Authors and Affiliations

  1. 1.University of CalabriaRendeItaly
  2. 2.R&D DepartmentUniCreditRomeItaly

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