Abstract

The scope of this paper is to test the adoption of a statistical model derived from Condensed Matter Physics, for the reconstruction of the structure of a social network. The inverse Potts model, traditionally applied to recursive observations of quantum states in an ensemble of particles, is here addressed to observations of the members' states in an organization and their (anti)correlations, thus inferring interactions as links among the members. Adopting proper (Bethe) approximations, such an inverse problem is showed to be tractable. Within an operational framework, this network-reconstruction method is tested for a small real-world social network, the Italian parliament. In this study case, it is easy to track statuses of the parliament members, using (co)sponsorships of law proposals as the initial dataset. In previous studies of similar activity-based networks, the graph structure was inferred directly from activity co-occurrences: here we compare our statistical reconstruction with such standard methods, outlining discrepancies and advantages.

Autore Pugliese

• Corallo Angelo

Tutti gli autori

• Bisconti C. , Corallo A. , Fortunato L. , Gentile A.A. , Massafra A. , Pellè P.

Titolo volume/Rivista

FRONTIERS IN PSYCHOLOGY

Anno di pubblicazione

2015

ISSN

1664-1078

ISBN

Non Disponibile

Numero di citazioni Wos

2

Ultimo Aggiornamento Citazioni

25/04/2018

Numero di citazioni Scopus

4

Ultimo Aggiornamento Citazioni

26/04/2018

Settori ERC

Non Disponibile

Codici ASJC

Non Disponibile