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arXiv · 2502.08567

Statistically validated projection of bipartite signed networks

Anna Gallo·Fabio Saracco·Tiziano Squartini
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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.

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Anna Gallo, Fabio Saracco, Tiziano Squartini. 2025-02-12. Statistically validated projection of bipartite signed networks. https://doi.org/10.1038/s44260-025-00043-1

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