Statistically Validated Networks in Bipartite Complex Systems
University of Palermo
Rosario N. Mantegna
Central European University; University of Palermo
University of Palermo - Department of Physics
University of Turku
Carnegie Mellon University - Department of Social and Decision Sciences; University of Palermo
September 28, 2010
Many complex systems present an intrinsic bipartite nature and are described and modeled in terms of networks. Bipartite networks are often very heterogeneous in the number of relationships that the elements of one set establish with the elements of the other set and the heterogeneity makes it very difficult to discriminate preferential links between the elements from randomly occurring links reflecting system heterogeneity. Here we introduce an unsupervised method to statistically validate each link of a projected network against a null hypothesis, which takes into account system heterogeneity. We apply our method to a biological, an economic and a social complex system. Our method is able to detect network structures which are informative about the organization and specialization of the investigated systems. Specifically, our method (i) identifies the preferential relationships between the elements, (ii) highlights the clustered structure of systems, and (iii) defines and classifies links according to the type of statistically validated relationships between the connected nodes.
Number of Pages in PDF File: 34
Keywords: Networks, Complex Systems
JEL Classification: C10, C80working papers series
Date posted: October 3, 2010
© 2013 Social Science Electronic Publishing, Inc. All Rights Reserved.
This page was processed by apollo7 in 0.688 seconds