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Community Detection in Multi-Partite Multi-Relational Networks Based on Information Compression

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Abstract

Community detection in uni-partite single-relational networks which contain only one type of nodes and edges has been extensively studied in the past decade. However, many real-world systems are naturally described as multi-partite multi-relational networks which contain multiple types of nodes and edges. In this paper, we propose an information compression based method for detecting communities in such networks. Specifically, based on the minimum description length (MDL) principle, we propose a quality function for evaluating partitions of a multi-partite multi-relational network into communities, and develop a heuristic algorithm for optimizing the quality function. We demonstrate that our method outperforms the state-of-the-art techniques in both synthetic and real-world networks.

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References

  1. Girvan, M. and Newman, M. E. J., “Community structure in social and biological networks,” Proc. Natl. Acad. Sci. USA, 99, pp. 7821–7826, 2002.

  2. Flake G. W., Lawrence S., Giles C. L., Coetzee F. M.: “Self-organization and identification of web communities,”. IEEE Computer 35(3), pp. 66–70 (2002)

    Article  Google Scholar 

  3. Dourisboure, Y., Geraci, F. and Pellegrini, M., “Extraction and classification of dense communities in the web,” in Proc. of the 16th International Conference on World Wide Web (Banff, Alberta, Canada), pp. 461–470, May 2007.

  4. Guimerà R., Amaral L. A. N.: “Functional cartography of complex metabolic networks,”. Nature 433, 895–900 (2005)

    Article  Google Scholar 

  5. Palla G., Derényi I., Farkas I., Vicsek T.: “Uncovering the overlapping community structure of complex networks in nature and society,”. Nature, 435(7043), 814–818 (2005)

    Article  Google Scholar 

  6. Fortunato S.: “Community detection in graphs,”. Physics Reports 486, 75–174 (2010)

    Article  MathSciNet  Google Scholar 

  7. Gulbahce N., Lehmann S.: “The art of community detection,”. BioEssays 30(10), 934–938 (2008)

    Article  Google Scholar 

  8. NewmanM. E. J.: Networks: An Introduction. Oxford University Press, New York (2010)

    Book  MATH  Google Scholar 

  9. Lancichinetti, A., Radicchi, F., Ramasco, J. J. and Fortunato, S., “Finding statistically significant communities in networks,” PloS one, 6, 4, e18961, 2011.

  10. Sun, Y., Yu, Y. and Han, J., “Ranking-based clustering of heterogeneous information networks with star network schema,” in Proc. of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Paris, France), pp. 797–806, Aug 2009.

  11. Comar P. M., Tan P. N., Jain A. K.: “A framework for joint community detection across multiple related networks,”. Neurocomputing, 76(1), 93–104 (2012)

    Article  Google Scholar 

  12. Lin, Y. R., Sun, J., Castro, P., Konuru, R., Sundaram, H. and Kelliher, A., “Metafac: community discovery via relational hypergraph factorization,” in Proc. of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Paris, France), pp. 527–535, Aug 2009.

  13. Liu X., Liu W., Murata T., Wakita K.: “A framework for community detection in heterogeneous multi-relational networks,”. Advances in Complex Systems, 17(1450018), 1–21 (2014)

    MathSciNet  Google Scholar 

  14. Rissanen J.: “Modelling by shortest data description,”. Automatica 14(5), 465–471 (1978)

    Article  MATH  Google Scholar 

  15. Newman M. E. J.: “Communities, modules and large-scale structure in networks,”. Nature Physics, 8(1), 25–31 (2012)

    Article  Google Scholar 

  16. Danon, L., Duch, J., D.-Guilera, A. and Arenas, A. “Comparing community structure identification,” J. Stat. Mech., P09008, 2005.

  17. Lancichinetti, A. and Fortunato, S., “Community detection algorithms: a comparative analysis,” Phys. Rev. E, 80, 056117, 2009.

  18. Orman, G. K., Labatut, V. and Cherifi, H., “Comparative evaluation of community detection algorithms: a topological approach,” J. Stat. Mech., P08001, 2012.

  19. Newman, M. E. J., “Fast algorithm for detecting community structure in networks,” Phys. Rev. E, 69, 066133, 2004.

  20. Clauset, A., Newman, M. E. J. and Moore, C., “Finding community structure in very large networks,” Phys. Rev. E, 7, 066111, 2004.

  21. Blondel, V. D., Guillaume, J. L., Lambiotte, R. and Lefebvre, E., “Fast unfolding of communities in large networks,” J. Stat. Mech., P10008, 2008.

  22. Liu X., Murata T.: “Advanced modularity-specialized label propagation algorithm for detecting communities in networks,”. Physica A, 389(7), 1493–1500 (2010)

    Article  Google Scholar 

  23. Newman, M. E. J., “Finding community structure in networks using the eigenvectors of matrices,” Phys. Rev. E, 74, 036104, 2006.

  24. Shen, H. and Cheng, X., “Spectral methods for the detection of network community structure: a comparative analysis,” J. Stat. Mech., P10020, 2010.

  25. Ball, B. Karrer, B. and Newman, M. E. J., “Efficient and principled method for detecting communities in networks,” Phys. Rev. E, 84, 036103, 2011.

  26. Karrer, B. and Newman, M. E. J., “Stochastic blockmodels and community structure in networks,” Phys. Rev. E, 83, 016107, 2011.

  27. Mucha P. J., Richardson T., Macon K., Porter M. A., Onnela J. P.: “Community structure in time-dependent, multiscale, and multiplex networks,”. Science 328, 876–878 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  28. Tang L., Liu H., Zhang J.: “Identifying evolving groups in dynamic multimode networks,”. IEEE Transactions on Knowledge and Data Engineering 24(1), 72–85 (2012)

    Article  Google Scholar 

  29. Tang L., Wang X., Liu H.: “Community detection via heterogeneous interaction analysis,”. Data Mining and Knowledge Discovery 25(1), 1–33 (2012)

    Article  MathSciNet  Google Scholar 

  30. Popescul, A., Flake, G.W., Lawrence, S., Ungar, L. H. and Giles, C. L., “Clustering and identifying temporal trends in document databases,” in Proc. of the IEEE Advances in Digital Libraries (Bethesda, MD, USA), pp. 173–182, May 2000.

  31. Dhillon, I. S., Mallela, S. and Modha, D. S., “Information-theoretic co-clustering,” in Proc. of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Washington, DC, USA), pp. 89–98, Aug 2003.

  32. Rosvall M., Bergstrom C. T.: “An information-theoretic framework for resolving community structure in complex networks,”. Proc. Natl. Acad. Sci. USA 104(18), 7327–7331 (2007)

    Article  Google Scholar 

  33. Sun, J., Faloutsos, C., Papadimitriou, S. and Yu, P. S., “Graphscope: parameterfree mining of large time-evolving graphs,” in Proc. of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (San Jose, CA, USA), pp. 687–696, Aug 2007.

  34. Liu X., Murata T.: “Detecting communities in k-partite k-uniform (hyper) networks,”. Journal of Computer Science and Technology, 26(5), 778–791 (2011)

    Article  MATH  Google Scholar 

  35. Long, B., Wu, X., Zhang, Z. and Yu, P. S., “Community learning by graph approximation,” in Proc. of the 7th IEEE International Conference on Data Mining, (Cambridge, MA, USA), pp. 232–241, Oct. 2007.

  36. Long, B., Zhang, Z., Yu, P. S. and Xu, T., “Clustering on complex graphs,” in Proc. of the 23rd National Conference on Artificial Intelligence (Chicago, IL, USA), pp. 659–664, 2008. 37. Rissanen, J. “A universal prior for integers and estimation by minimum description length,”. The Annals of Statistics 11, 2, 416–43 1983.

  37. Rissanen J.: “A universal prior for integers and estimation by minimum description length,” The Annals of Statistics, 11(2), 416–431 1983

    Article  MathSciNet  MATH  Google Scholar 

  38. Waltman, L. and van Eck, N. J., “A smart local moving algorithm for largescale modularity-based community detection,” The European Physical Journal B, 86, 11, 471, 2013.

  39. Rotta, R. and Noack, A., “Multilevel local search algorithms for modularity clustering,” ACM Journal of Experimental Algorithmics, 16, 2, 2.3, 2011.

  40. Schuetz, P. and Caflisch, A., “Efficient modularity optimization by multistep greedy algorithm and vertex refinement,” Phys. Rev. E, 77, 046112, 2008.

  41. Arenas, A., Duch, J., Fernández, A. and Gómez, S., “Size reduction of complex networks preserving modularity,” New Journal of Physics, 9 176, 2007.

  42. Lancichinetti, A., Fortunato, S. and Radicchi, F., “Benchmark graphs for testing community detection algorithms,” Phys. Rev. E, 78, 046110, 2008.

  43. Lancichinetti, A. and Fortunato, S., “Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities,” Phys. Rev. E, 80, 016118, 2009.

  44. Fred, A. L. N. and Jain, A. K., “Robust data clustering,” in Proc. of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Madison, WI, USA), pp. 128–133, Jun 2003.

  45. Ruan, Y., Fuhry, D. and Parthasarathy, S., “Efficient community detection in large networks using content and links,” in Proc. of the 22nd International Conference on World Wide Web (Rio de Janeiro, Brazil), pp. 1089–1098, May 2013.

  46. Günnemann, S., Boden, B. and Seidl, T., “Db-csc: a density-based approach for subspace clustering in graphs with feature vectors,” in Proc. of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (Athens, Greece), pp. 565–580, Sep 2011.

  47. Günnemann S., Färber I., Boden B., Seidl T.: “Gamer: a synthesis of subspace clustering and dense subgraph mining,”. Knowledge and information systems, 40(2), 243–278 (2013)

    Article  Google Scholar 

  48. Günnemann, S., Boden, B., Färber, I. and Seidl, T., “Efficient mining of combined subspace and subgraph clusters in graphs with feature vectors,” in Proc. of the 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining (Gold Coast, Australia), pp. 261–275, Apr 2013.

  49. Boden, B., Ester, M. and Seidl, T., “Density-based subspace clustering in heterogeneous networks,” in Proc. of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (Nancy, France), pp. 149–164, Sep 2014.

  50. Yang, J., McAuley, J. and Leskovec, J., “Community detection in networks with node attributes,” in Proc. of the 13th IEEE International Conference on Data Mining, (Dallas, TX, USA), pp. 1151–1156, Dec 2013.

  51. Barber, M. J., “Modularity and community detection in bipartite networks,” Phys. Rev. E, 76, 066102, 2007.

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Authors and Affiliations

  1. Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro, Tokyo, 152-8552, Japan

    Xin Liu, Weichu Liu, Tsuyoshi Murata & Ken Wakita

  2. JST CREST K’s Gobancho, 7, Gobancho, Chiyoda, Tokyo, 102-0076, Japan

    Xin Liu & Ken Wakita

  3. Wuhan University of Technology, 122 Luoshi Road, Wuhan, Hubei, 430070, China

    Xin Liu

Authors
  1. Xin Liu
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  2. Weichu Liu
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  3. Tsuyoshi Murata
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  4. Ken Wakita
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Correspondence to Xin Liu.

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Liu, X., Liu, W., Murata, T. et al. Community Detection in Multi-Partite Multi-Relational Networks Based on Information Compression. New Gener. Comput. 34, 153–176 (2016). https://doi.org/10.1007/s00354-016-0206-1

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