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Latent Space Generative Model for Bipartite Networks

  • Demival Vasques FilhoEmail author
  • Dion R. J. O’Neale
Conference paper
  • 65 Downloads
Part of the Springer Proceedings in Complexity book series (SPCOM)

Abstract

Generative network models are useful for understanding the mechanisms that operate in network formation and are used across several areas of knowledge. However, when it comes to bipartite networks—a class of network frequently encountered in social systems, among others—generative models are practically non-existent. Here, we propose a latent space generative model for bipartite networks growing in a hyperbolic plane. It is an extension of a model previously proposed for one-mode networks, based on a maximum entropy approach. We show that, by reproducing bipartite structural properties, such as degree distributions and small cycles, bipartite networks can be better modelled and properties of one-mode projected network can be naturally assessed.

Keywords

Bipartite networks Generative models Hyperbolic geometry Maximum entropy 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Leibniz-Institut für Europäische GeschichteMainzGermany
  2. 2.Te Pūnaha Matatini, Department of PhysicsUniversity of AucklandAucklandNew Zealand
  3. 3.Physics DepartmentUniversity of AucklandAucklandNew Zealand
  4. 4.Te Pūnaha Matatini, Centre of Research ExcellenceAucklandNew Zealand

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