TY  - RPRT
Y1  - 2014/04//
N1  - Published in PloS One (ISSN 1932-6203): http://dx.doi.org/10.1371/journal.pone.0109507
N2  - 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. 
TI  - Cluster analysis of weighted bipartite networks: a new copula-based approach
A1  - Chessa, Alessandro
A1  - Crimaldi, Irene
A1  - Riccaboni, Massimo
A1  - Trapin, Luca
ID  - eprints2189
M1  - imt_eic_working_paper
AV  - public
KW  - clustering
KW  -  complex network
KW  -  copula function
KW  -  positional analysis
KW  -  weighted bipartite network.
PB  - IMT Institute for Advanced Studies Lucca
UR  - http://eprints.imtlucca.it/2189/
EP  - 22
ER  -