Statistically validated projection of bipartite signed networks

TL;DR

This paper introduces an unsupervised statistical method to project bipartite signed networks, revealing meaningful structures by validating significant relationships and accounting for intra-layer linkages.

Contribution

It presents a novel algorithm for statistically validated projections of bipartite signed networks, addressing information shortages and detecting genuine mesoscopic structures.

Findings

Successfully applied to synthetic and real-world data

Detected non-trivial mesoscopic structures

Revealed self-organization in network patterns

Abstract

Bipartite networks provide a major insight into the organisation of many real-world systems. One of the most relevant issues encountered when modelling a bipartite network is that of facing the information shortage concerning intra-layer linkages. In the present contribution, we propose an unsupervised algorithm to obtain statistically validated projections of bipartite signed networks, according to which any two nodes sharing a statistically significant number of concordant (discordant) relationships are connected by a positive (negative) edge. Our algorithm outputs a matrix of link-specific $p-$values, from which a validated projection can be obtained upon running a multiple-hypothesis testing procedure. After testing our method on synthetic configurations output by a fully controllable generative model, we apply it to several real-world configurations: in all cases, non-trivial mesoscopic structures, induced by relationships that cannot be traced back to the constraints defining the employed benchmarks, hence revealing genuine traces of self-organisation, are detected.