Statistically validated networks in bipartite complex systems

• Published in: PloS one 2011-03, Vol.6 (3), p.e17994-e17994
• Main Authors: Tumminello, Michele, Miccichè, Salvatore, Lillo, Fabrizio, Piilo, Jyrki, Mantegna, Rosario N
• Format: Article
• Language: English
• Subjects: Actors; Biology; Collaboration; Complex systems; Heterogeneity; Hypotheses; Hypothesis testing; Mathematics; Models, Biological; Models, Theoretical; Null hypothesis; Physics; Protein Interaction Mapping; Social and Behavioral Sciences; Specialization; Stock exchanges; Taxonomy
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0017994

Many complex systems present an intrinsic bipartite structure where elements of one set link to elements of the second set. In these complex systems, such as the system of actors and movies, elements of one set are qualitatively different than elements of the other set. The properties of these complex systems are typically investigated by constructing and analyzing a projected network on one of the two sets (for example the actor network or the movie network). Complex systems are often very heterogeneous in the number of relationships that the elements of one set establish with the elements of the other set, and this heterogeneity makes it very difficult to discriminate links of the projected network that are just reflecting system's heterogeneity from links relevant to unveil the properties of the system. Here we introduce an unsupervised method to statistically validate each link of a projected network against a null hypothesis that takes into account system heterogeneity. We apply the method to a biological, an economic and a social complex system. The method we propose is able to detect network structures which are very informative about the organization and specialization of the investigated systems, and identifies those relationships between elements of the projected network that cannot be explained simply by system heterogeneity. We also show that our method applies to bipartite systems in which different relationships might have different qualitative nature, generating statistically validated networks in which such difference is preserved.