Abstract
This study proposes ComSim, a new algorithm to detect communities in bipartite networks. This approach generates a partition of \(\top \) nodes by relying on similarity between the nodes in terms of links towards \(\bot \) nodes. In order to show the relevance of this approach, we implemented and tested the algorithm on 2 small datasets equipped with a ground-truth partition of the nodes. It turns out that, compared to 3 baseline algorithms used in the context of bipartite graph, ComSim proposes the best communities. In addition, we tested the algorithm on a large scale network. Results show that ComSim has good performances, close in time to Louvain. Besides, a qualitative investigation of the communities detected by ComSim reveals that it proposes more balanced communities.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
We use a similar definition of \(N_\bot (v)\) for \(v\in \bot \).
- 2.
Depending on the similarity function used, the projection might result in a directed weighted graph if \(\theta \) is not symmetric.
- 3.
For an homogeneous analysis, we removed all TV shows and documentaries and kept only the 7 first actors listed in the casting.
- 4.
Since lpBRIM does not scale up to the size of IMDb, we avoid mentioning this approach in the rest of the study.
References
Adamic, L.A., Adar, E.: Friends and neighbors on the web. Soc. Netw. 25(3), 211–230 (2003)
Ahn, Y.Y., Ahnert, S.E., Bagrow, J.P., Barabási, A.L.: Flavor network and the principles of food pairing. Sci. Rep. 1 (2011)
Barber, M.J.: Modularity and community detection in bipartite networks. Phys. Rev. E 76(6), 066102 (2007). https://doi.org/10.1103/PhysRevE.76.066102
Battiston, S., Catanzaro, M.: Statistical properties of corporate board and director networks. Eur. Phys. J. B Condens. Matter Complex Syst. 38(2), 345–352 (2004)
Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech. Theory Exp. 2008(10), P10008 (2008)
i Cancho, R.F., Solé, R.V.: The small world of human language. Proc. R. Soc. Lond. Ser. B Biol. Sci. 268(1482), 2261–2265 (2001)
Davis, A., Gardner, B.B., Gardner, M.R.: Deep South; A Social Anthropological Study of Caste and Class. The University of Chicago Press, Chicago (1941)
Fortunato, S.: Community detection in graphs. Phys. Rep. 486(3), 75–174 (2010)
Freeman, L.C.: Finding social groups: A meta-analysis of the southern women data (2003)
Green, E.C.: Southern Strategies: Southern Women and the Woman Suffrage Question. University of North Carolina Press (1997)
Jaccard, P.: Le coefficient generique et le coefficient de communaute dans la flore marocaine. Impr. Commerciale (1926)
Lancichinetti, A., Fortunato, S., Kertész, J.: Detecting the overlapping and hierarchical community structure in complex networks. New J. Phys. 11(3), 033015 (2009)
Lang, K.: Newsweeder: learning to filter netnews. In: Proceedings of the Twelfth International Conference on Machine Learning, pp. 331–339 (1995)
Larremore, D.B., Clauset, A., Jacobs, A.Z.: Efficiently inferring community structure in bipartite networks. Phys. Rev. E 90(1), 012805 (2014)
Le Fessant, F., Handurukande, S., Kermarrec, A.M., Massoulié, L.: Clustering in peer-to-peer file sharing workloads. In: Peer-to-Peer Systems III, pp. 217–226. Springer (2005)
Lehmann, S., Schwartz, M., Hansen, L.K.: Biclique communities. Phys. Rev. E 78(1), 016108 (2008)
Liu, X., Murata, T.: Community detection in large-scale bipartite networks. In: Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT ’09, vol. 1, pp. 50–57. IEEE Computer Society, Washington, DC, USA (2009). https://doi.org/10.1109/WI-IAT.2009.15
Maas, A.L., Daly, R.E., Pham, P.T., Huang, D., Ng, A.Y., Potts, C.: Learning word vectors for sentiment analysis. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 142–150. Association for Computational Linguistics, Portland, Oregon, USA. http://www.aclweb.org/anthology/P11-1015 (2011)
Newman, M.E.: Modularity and community structure in networks. Proc. Natl. Acad. Sci. 103(23), 8577–8582 (2006)
Newman, M.E., Strogatz, S.H., Watts, D.J.: Random graphs with arbitrary degree distributions. Phys. Rev. E 64 (2001)
Newman, M.E., Watts, D.J., Strogatz, S.H.: Random graph models of social networks. Proc. Natl. Acad. Sci. U. S. A. 99(Suppl 1), 2566–2572 (2002)
Prat-Pérez, A., Dominguez-Sal, D., Larriba-Pey, J.L.: High quality, scalable and parallel community detection for large real graphs. In: Proceedings of the 23rd international conference on World wide web, pp. 225–236. ACM (2014)
Prieur, C., Cardon, D., Beuscart, J.S., Pissard, N., Pons, P.: The stength of weak cooperation: a case study on flickr (2008). arXiv preprint arXiv:0802.2317
Rosvall, M., Axelsson, D., Bergstrom, C.T.: The map equation. Eur. Phys. J. Spec. Top. 178(1), 13–23 (2009)
Watts, D.J., Strogatz, S.H.: Collective dynamics of ‘small-world’ networks. Nature 393(6684), 440–442 (1998)
Yang, J., Leskovec, J.: Overlapping community detection at scale: a nonnegative matrix factorization approach. In: Proceedings of the sixth ACM international conference on Web search and data mining, pp. 587–596. ACM (2013)
Yang, J., Leskovec, J.: Defining and evaluating network communities based on ground-truth. Knowl. Inf. Syst. 42(1), 181–213 (2015)
Zhou, T., Lü, L., Zhang, Y.C.: Predicting missing links via local information. Eur. Phys. J. B Condens. Matter Complex Syst. 71(4), 623–630 (2009)
Acknowledgements
This work is funded in part by the European Commission H2020 FETPROACT 2016–2017 program under grant 732942 (ODYCCEUS), by the ANR (French National Agency of Research) under grants ANR-15-CE38-0001 (AlgoDiv) and ANR-13-CORD-0017-01 (CODDDE), by the French program “PIA—Usages, services et contenus innovants” under grant O18062-44430 (REQUEST), and by the Ile-de-France program FUI21 under grant 16010629 (iTRAC).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Tackx, R., Tarissan, F., Guillaume, JL. (2018). ComSim: A Bipartite Community Detection Algorithm Using Cycle and Node’s Similarity. In: Cherifi, C., Cherifi, H., Karsai, M., Musolesi, M. (eds) Complex Networks & Their Applications VI. COMPLEX NETWORKS 2017. Studies in Computational Intelligence, vol 689. Springer, Cham. https://doi.org/10.1007/978-3-319-72150-7_23
Download citation
DOI: https://doi.org/10.1007/978-3-319-72150-7_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-72149-1
Online ISBN: 978-3-319-72150-7
eBook Packages: EngineeringEngineering (R0)