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

Alessandro Chessa (), Irene Crimaldi (), Massimo Riccaboni () and Luca Trapin ()
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Alessandro Chessa: IMT School for Advanced Studies Lucca
Irene Crimaldi: IMT School for Advanced Studies Lucca

No 3/2014, Working Papers from IMT School for Advanced Studies Lucca

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. The main contribution of our work is twofold. First, 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. Second, 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 real-valued matrices. We validate it with simulations and applications to real-world data.

Keywords: Clustering; complex network; copula function; positional analysis; weighted bipartite network (search for similar items in EconPapers)
JEL-codes: C6 F1 (search for similar items in EconPapers)
Pages: 22 pages
Date: 2014-04, Revised 2014-04
New Economics Papers: this item is included in nep-cmp, nep-ecm, nep-net and nep-ore
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Published in EIC working paper series

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