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Bipartite graphs as models of complex networks

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  • Guillaume, Jean-Loup
  • Latapy, Matthieu

Abstract

It appeared recently that the classical random graph model used to represent real-world complex networks does not capture their main properties. Since then, various attempts have been made to provide accurate models. We study here a model which achieves the following challenges: it produces graphs which have the three main wanted properties (clustering, degree distribution, average distance), it is based on some real-world observations, and it is sufficiently simple to make it possible to prove its main properties. This model consists in sampling a random bipartite graph with prescribed degree distribution. Indeed, we show that any complex network may be viewed as a bipartite graph with some specific characteristics, and that its main properties may be viewed as consequences of this underlying structure. We also propose a growing model based on this observation.

Suggested Citation

  • Guillaume, Jean-Loup & Latapy, Matthieu, 2006. "Bipartite graphs as models of complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 371(2), pages 795-813.
    • Handle: RePEc:eee:phsmap:v:371:y:2006:i:2:p:795-813
    DOI: 10.1016/j.physa.2006.04.047
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    References listed on IDEAS

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    Cited by:

    1. Mukherjee, Animesh & Choudhury, Monojit & Ganguly, Niloy, 2011. "Understanding how both the partitions of a bipartite network affect its one-mode projection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(20), pages 3602-3607.
    2. Jorge Peña & Yannick Rochat, 2012. "Bipartite Graphs as Models of Population Structures in Evolutionary Multiplayer Games," PLOS ONE, Public Library of Science, vol. 7(9), pages 1-13, September.
    3. Erik M Volz & Joel C Miller & Alison Galvani & Lauren Ancel Meyers, 2011. "Effects of Heterogeneous and Clustered Contact Patterns on Infectious Disease Dynamics," PLOS Computational Biology, Public Library of Science, vol. 7(6), pages 1-13, June.
    4. STANKOVA, Marija & MARTENS, David & PROVOST, Foster, 2015. "Classification over bipartite graphs through projection," Working Papers 2015001, University of Antwerp, Faculty of Business and Economics.
    5. Rita Strohmaier & Marlies Schuetz & Simone Vannuccini, 2019. "A systemic perspective on socioeconomic transformation in the digital age," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 46(3), pages 361-378, September.
    6. Hao-Nan Yang & Xin-Jian Xu & Haili Liang & Xiaofan Wang, 2020. "A comparative study of online communities and popularity of BBS in four Chinese universities," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-16, June.
    7. Ben Curran & Kyle Higham & Elisenda Ortiz & Demival Vasques Filho, 2018. "Look who’s talking: Two-mode networks as representations of a topic model of New Zealand parliamentary speeches," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-16, June.
    8. Liu, Chengliang & Duan, Dezhong, 2020. "Spatial inequality of bus transit dependence on urban streets and its relationships with socioeconomic intensities: A tale of two megacities in China," Journal of Transport Geography, Elsevier, vol. 86(C).

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