Community Detection in Bipartite Network: A Modified Coarsening Approach

  • Alan ValejoEmail author
  • Vinícius Ferreira
  • Maria C. F. de Oliveira
  • Alneu de Andrade Lopes
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 795)


Interest in algorithms for community detection in networked systems has increased over the last decade, mostly motivated by a search for scalable solutions capable of handling large-scale networks. Multilevel approaches provide a potential solution to scalability, as they reduce the cost of a community detection algorithm by applying it to a coarsened version of the original network. The solution obtained in the small-scale network is then projected back to the original large-scale model to obtain the desired solution. However, standard multilevel methods are not directly applicable to bipartite networks and there is a gap in existing literature on multilevel optimization applied to such networks. This article addresses this gap and introduces a novel multilevel method based on one-mode projection that allows executing traditional multilevel methods in bipartite network models. The approach has been validated with an algorithm for community detection that solves the Barber’s modularity problem. We show it can scale a target algorithm to handling larger networks, whilst preserving solution accuracy.



Author A. Valejo is supported by a scholarship from the Brazilian Federal Agency for Support and Evaluation of Graduate Education (CAPES). This work has been partially supported by the State of São Paulo Research Foundation (FAPESP) grants 17/05838-3; and the Brazilian Federal Research Council (CNPq) grants 302645/2015-2 and 3056-96/2013-0.


  1. 1.
    Abou-Rjeili, A., Karypis, G.: Multilevel algorithms for partitioning power-law graphs. In: Proceedings of the 20th International Parallel and Distributed Processing Symposium, pp. 124–135 (2006)Google Scholar
  2. 2.
    Alzahrani, T., Horadam, K.J.: Community detection in bipartite networks: algorithms and case studies. In: Lü, J., Yu, X., Chen, G., Yu, W. (eds.) Complex Systems and Networks. UCS, pp. 25–50. Springer, Heidelberg (2016). Scholar
  3. 3.
    Banos, R., Gil, C., Ortega, J., Montoya, F.G.: Parallel heuristic search in multilevel graph partitioning. In: Proceedings of the 12th Euromicro Conference on Parallel, Distributed and Network-Based Processing, pp. 88–95 (2004)Google Scholar
  4. 4.
    Baños, R., Gil, C., Ortega, J., Montoya, F.G.: A parallel multilevel metaheuristic for graph partitioning. J. Heuristics 10(3), 315–336 (2004)CrossRefGoogle Scholar
  5. 5.
    Beckett, S.J.: Improved community detection in weighted bipartite networks. R. Soc. Open Sci. 3(1), 18 (2016)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Demšar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)MathSciNetzbMATHGoogle Scholar
  7. 7.
    Djidjev, H.N.: A scalable multilevel algorithm for graph clustering and community structure detection. In: Aiello, W., Broder, A., Janssen, J., Milios, E. (eds.) WAW 2006. LNCS, vol. 4936, pp. 117–128. Springer, Heidelberg (2008). Scholar
  8. 8.
    Djidjev, H.N., Onus, M.: Scalable and accurate graph clustering and community structure detection. IEEE Trans. Parallel Distrib. Syst. 24(5), 1022–1029 (2013)CrossRefGoogle Scholar
  9. 9.
    Dormann, C.F., Strauss, R.: Detecting modules in quantitative bipartite networks: the QuaBiMo algorithm. arXiv preprint 1304.3218 (2013)Google Scholar
  10. 10.
    Dormann, C.F., Strauss, R.: A method for detecting modules in quantitative bipartite networks. Meth. Ecol. Evol. 5(1), 90–98 (2014)CrossRefGoogle Scholar
  11. 11.
    Erciye, K., Alp, A., Marshall, G.: Serial and parallel multilevel graph partitioning using fixed centers. In: Proceedings of the 31st Conference on Current Trends in Theory and Practice of Computer Science, pp. 127–136 (2005)Google Scholar
  12. 12.
    Fortunato, S.: Community detection in graphs. Phys. Rep. 486(3–5), 75–174 (2010)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. Proc. Natl. Acad. Sci. USA 99, 7821–7826 (2002)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Jeong, H., Tombor, B., Albert, R., Oltvai, Z.N., Barabasi, A.L.: The large-scale organization of metabolic networks. Nature 407(6804), 651–654 (2000)CrossRefGoogle Scholar
  15. 15.
    Karypis, G., Kumar, V.: A fast and high quality multilevel scheme for partitioning irregular graphs. SIAM J. Sci. Comput. 20(1), 359–392 (1998)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Labatut, V.: Generalized measures for the evaluation of community detection methods. CoRR abs/1303.5441 (2013)Google Scholar
  17. 17.
    Larremore, D.B., Clauset, A., Jacobs, A.Z.: Efficiently inferring community structure in bipartite networks. CoRR abs/1403.2933 (2014)Google Scholar
  18. 18.
    LaSalle, D., Karypis, G.: Multi-threaded graph partitioning. In: Proceedings of the 27th IEEE International Parallel and Distributed Processing Symposium, pp. 225–236 (2013)Google Scholar
  19. 19.
    Lasalle, D., Karypis, G.: Multi-threaded modularity based graph clustering using the multilevel paradigm. J. Parallel Distrib. Comput. 76, 66–80 (2015)CrossRefGoogle Scholar
  20. 20.
    Mahmoud, H., Masulli, F., Rovetta, S., Russo, G.: Community detection in protein-protein interaction networks using spectral and graph approaches. In: Formenti, E., Tagliaferri, R., Wit, E. (eds.) CIBB 2013 2013. LNCS, vol. 8452, pp. 62–75. Springer, Cham (2014). Scholar
  21. 21.
    Newman, M.E.J.: The structure of scientific collaboration networks. Proc. Natl. Acad. Sci. USA 98(2), 404–409 (2001)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Noack, A., Rotta, R.: Multi-level algorithms for modularity clustering. In: Vahrenhold, J. (ed.) SEA 2009. LNCS, vol. 5526, pp. 257–268. Springer, Heidelberg (2009). Scholar
  23. 23.
    Opsahl, T.: Triadic closure in two-mode networks: redefining the global and local clustering coefficients. Soc. Netw. 35, 159–167 (2013)CrossRefGoogle Scholar
  24. 24.
    Padrón, B., Nogales, M., Traveset, A.: Alternative approaches of transforming bimodal into unimodal mutualistic networks. The usefulness of preserving weighted information. Basic Appl. Ecol. 12(8), 713–721 (2011)CrossRefGoogle Scholar
  25. 25.
    Rossi, R.G., de Andrade Lopes, A., Rezende, S.O.: Optimization and label propagation in bipartite heterogeneous networks to improve transductive classification of texts. Inf. Process. Manage. 52(2), 217–257 (2016)CrossRefGoogle Scholar
  26. 26.
    Rotta, R., Noack, A.: Multilevel local search algorithms for modularity clustering. J. Exp. Algorithmics 16(2), 2–3 (2011)MathSciNetzbMATHGoogle Scholar
  27. 27.
    Schuetz, P., Caflisch, A.: Efficient modularity optimization by multistep greedy algorithm and vertex mover refinement. Physical Rev. E Stat. Nonlinear Soft Matter Phys. 77(4), 1–7 (2008)CrossRefGoogle Scholar
  28. 28.
    Schweitz, E.A., Agrawal, D.P.: A parallelization domain oriented multilevel graph partitioner. IEEE Trans. Comput. 51(12), 1435–1441 (2002)MathSciNetCrossRefGoogle Scholar
  29. 29.
    Thébault, E.: Identifying compartments in presence-absence matrices and bipartite networks: insights into modularity measures. J. Biogeogr. 40(4), 759–768 (2013)CrossRefGoogle Scholar
  30. 30.
    Trifunovic, A., Knottenbelt, W.J.: A parallel algorithm for multilevel k-way hypergraph partitioning. In: Proceedings of the Third International Symposium on Parallel and Distributed Computing, pp. 114–121. IEEE (2004)Google Scholar
  31. 31.
    Trifunovic, A., Knottenbelt, W.J.: Parkway 2.0: a parallel multilevel hypergraph partitioning tool. In: Aykanat, C., Dayar, T., Körpeoğlu, İ. (eds.) Proceedings of the 19th International Symposium, Kemer-Antalya, Turkey, 27–29 October 2004Google Scholar
  32. 32.
    Valejo, A., Drury, B., Valverde-Rebaza, J., de Andrade Lopes, A.: Identification of related Brazilian Portuguese verb groups using overlapping community detection. In: Baptista, J., Mamede, N., Candeias, S., Paraboni, I., Pardo, T.A.S., Volpe Nunes, M.G. (eds.) PROPOR 2014. LNCS (LNAI), vol. 8775, pp. 292–297. Springer, Cham (2014). Scholar
  33. 33.
    Valejo, A., Ferreira, V., Rocha, G.P., Oliveira, M.C.F., de Andrade Lopes, A.: One-mode projection-based multilevel approach for community detection in bipartite networks. In: Proceedings of the 4th Annual International Symposium on Information Management and Big Data, Track on Social Network and Media Analysis and Mining (SNMAM) (2017)Google Scholar
  34. 34.
    Valejo, A., Rebaza, J.C.V., de Andrade Lopes, A.: A multilevel approach for overlapping community detection. In: Proceedings of the 2014 Brazilian Conference on Intelligent Systems (2014)Google Scholar
  35. 35.
    Valejo, A., Valverde-Rebaza, J., Drury, B., de Andrade Lopes, A.: Multilevel refinement based on neighborhood similarity. In: Proceedings of the 18th International Database Engineering and Applications Symposium, pp. 67–76 (2014)Google Scholar
  36. 36.
    Watts, D.J., Strogatz, S.H.: Collective dynamics of ‘small-world’ networks. Nature 393(6684), 409–410 (1998)CrossRefGoogle Scholar
  37. 37.
    Ye, Z., Hu, S., Yu, J.: Adaptive clustering algorithm for community detection in complex networks. Phys. Rev. E 78, 046110 (2008)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Alan Valejo
    • 1
    Email author
  • Vinícius Ferreira
    • 1
  • Maria C. F. de Oliveira
    • 1
  • Alneu de Andrade Lopes
    • 1
  1. 1.Institute of Mathematical and Computer Sciences (ICMC)University of São Paulo (USP)São CarlosBrazil

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