Statistically validated projection of bipartite signed networks
arXiv:2502.08567 · doi:10.1038/s44260-025-00043-1
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 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.
35 pages, 10 figures, 3 tables
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