Cluster analysis of weighted bipartite networks: A new copula-based approach

Luca Trapin, Alessandro Chessa, Irene Crimaldi, Massimo Riccaboni

Risultato della ricerca: Contributo in rivistaArticolo in rivista

5 Citazioni (Scopus)

Abstract

In this work we are interested in identifying clusters of "positional equivalent" actors, i.e. actors who play a similar role in a system. In particular, we analyze weighted bipartite networks that describes the relationships between actors on one side and features or traits on the other, together with the intensity level to which actors show their features. We develop a methodological approach that takes into account the underlying multivariate dependence among groups of actors. The idea is that positions in a network could be defined on the basis of the similar intensity levels that the actors exhibit in expressing some features, instead of just considering relationships that actors hold with each others. Moreover, we propose a new clustering procedure that exploits the potentiality of copula functions, a mathematical instrument for the modelization of the stochastic dependence structure. Our clustering algorithm can be applied both to binary and realvalued matrices. We validate it with simulations and applications to real-world data.
Lingua originaleEnglish
pagine (da-a)N/A-N/A
RivistaPLoS One
Volume9
DOI
Stato di pubblicazionePubblicato - 2014

Keywords

  • Agricultural and Biological Sciences (all)
  • Algorithms
  • Biochemistry, Genetics and Molecular Biology (all)
  • Cluster Analysis
  • Computer Simulation
  • Humans
  • Medicine (all)
  • Models, Theoretical

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