A primer on statistically validated networks

Abstract

In this contribution we discuss some approaches of network analysis providing information about single links or single nodes with respect to a null hypothesis taking into account the heterogeneity of the system empirically observed. With this approach, a selection of nodes and links is feasible when the null hypothesis is statistically rejected. We focus our discussion on approaches using (i) the so-called disparity filter and (ii) statistically validated network in bipartite networks. For both methods we discuss the importance of using multiple hypothesis test correction. Specific applications of statistically validated networks are discussed. We also discuss how statistically validated networks can be used to (i) pre-process large sets of data and (ii) detect cores of communities that are forming the most close-knit and stable subsets of clusters of nodes present in a complex system.