Skip to main content
SpringerLink
Log in

Community Detection in Bipartite Network: A Modified Coarsening Approach

  • Conference paper
  • First Online:
Book cover Information Management and Big Data (SIMBig 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 795))

Included in the following conference series:

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://github.com/sjbeckett/weighted-modularity-LPAwbPLUS.

  2. 2.

    https://github.com/alanvalejo/opm.

  3. 3.

    http://igraph.org/python/.

References

  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. 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). https://doi.org/10.1007/978-3-662-47824-0_2

    Chapter  Google Scholar 

  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. Baños, R., Gil, C., Ortega, J., Montoya, F.G.: A parallel multilevel metaheuristic for graph partitioning. J. Heuristics 10(3), 315–336 (2004)

    Article  Google Scholar 

  5. Beckett, S.J.: Improved community detection in weighted bipartite networks. R. Soc. Open Sci. 3(1), 18 (2016)

    Article  MathSciNet  Google Scholar 

  6. Demšar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)

    MathSciNet  MATH  Google Scholar 

  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). https://doi.org/10.1007/978-3-540-78808-9_11

    Chapter  MATH  Google Scholar 

  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)

    Article  Google Scholar 

  9. Dormann, C.F., Strauss, R.: Detecting modules in quantitative bipartite networks: the QuaBiMo algorithm. arXiv preprint 1304.3218 (2013)

    Google Scholar 

  10. Dormann, C.F., Strauss, R.: A method for detecting modules in quantitative bipartite networks. Meth. Ecol. Evol. 5(1), 90–98 (2014)

    Article  Google Scholar 

  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. Fortunato, S.: Community detection in graphs. Phys. Rep. 486(3–5), 75–174 (2010)

    Article  MathSciNet  Google Scholar 

  13. Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. Proc. Natl. Acad. Sci. USA 99, 7821–7826 (2002)

    Article  MathSciNet  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  16. Labatut, V.: Generalized measures for the evaluation of community detection methods. CoRR abs/1303.5441 (2013)

    Google Scholar 

  17. Larremore, D.B., Clauset, A., Jacobs, A.Z.: Efficiently inferring community structure in bipartite networks. CoRR abs/1403.2933 (2014)

    Google Scholar 

  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. Lasalle, D., Karypis, G.: Multi-threaded modularity based graph clustering using the multilevel paradigm. J. Parallel Distrib. Comput. 76, 66–80 (2015)

    Article  Google Scholar 

  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). https://doi.org/10.1007/978-3-319-09042-9_5

    Chapter  Google Scholar 

  21. Newman, M.E.J.: The structure of scientific collaboration networks. Proc. Natl. Acad. Sci. USA 98(2), 404–409 (2001)

    Article  MathSciNet  Google Scholar 

  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). https://doi.org/10.1007/978-3-642-02011-7_24

    Chapter  Google Scholar 

  23. Opsahl, T.: Triadic closure in two-mode networks: redefining the global and local clustering coefficients. Soc. Netw. 35, 159–167 (2013)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  26. Rotta, R., Noack, A.: Multilevel local search algorithms for modularity clustering. J. Exp. Algorithmics 16(2), 2–3 (2011)

    MathSciNet  MATH  Google Scholar 

  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)

    Article  Google Scholar 

  28. Schweitz, E.A., Agrawal, D.P.: A parallelization domain oriented multilevel graph partitioner. IEEE Trans. Comput. 51(12), 1435–1441 (2002)

    Article  MathSciNet  Google Scholar 

  29. Thébault, E.: Identifying compartments in presence-absence matrices and bipartite networks: insights into modularity measures. J. Biogeogr. 40(4), 759–768 (2013)

    Article  Google Scholar 

  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. 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 2004

    Google Scholar 

  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). https://doi.org/10.1007/978-3-319-09761-9_35

    Chapter  Google Scholar 

  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. 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. 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. Watts, D.J., Strogatz, S.H.: Collective dynamics of ‘small-world’ networks. Nature 393(6684), 409–410 (1998)

    Article  Google Scholar 

  37. Ye, Z., Hu, S., Yu, J.: Adaptive clustering algorithm for community detection in complex networks. Phys. Rev. E 78, 046110 (2008)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgments

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.

Author information

Authors and Affiliations

  1. Institute of Mathematical and Computer Sciences (ICMC), University of São Paulo (USP), São Carlos, SP, 14560-970, Brazil

    Alan Valejo, Vinícius Ferreira, Maria C. F. de Oliveira & Alneu de Andrade Lopes

Authors
  1. Alan Valejo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vinícius Ferreira
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Maria C. F. de Oliveira
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Alneu de Andrade Lopes
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alan Valejo .

Editor information

Editors and Affiliations

  1. University of Florida, Gainesville, Florida, USA

    Juan Antonio Lossio-Ventura

  2. Universidad del Pacífico, Lima, Peru

    Hugo Alatrista-Salas

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Valejo, A., Ferreira, V., de Oliveira, M.C.F., de Andrade Lopes, A. (2018). Community Detection in Bipartite Network: A Modified Coarsening Approach. In: Lossio-Ventura, J., Alatrista-Salas, H. (eds) Information Management and Big Data. SIMBig 2017. Communications in Computer and Information Science, vol 795. Springer, Cham. https://doi.org/10.1007/978-3-319-90596-9_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-90596-9_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-90595-2

  • Online ISBN: 978-3-319-90596-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation