Thursday, September 18 2025

2 pm – Aula Fermi

Abstract: Statistically validated projection of bipartite signed networks

Bipartite networks provide a major insight into the organisation of many real-world systems. One of the most relevant issues encountered when modeling a bipartite network is that of devising a way to obtain a monopartite projection that preserves as much as possible the information encoded into the original structure. Hereby, 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) motifs are connected by a positive (negative) edge. Statistical significance is assessed using four different Exponential Random Graph Models, producing link-specific p-values that are filtered through multiple testing correction. After testing our method on synthetic configurations output by a fully controllable generative model, we apply it to three social 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, are detected.

The speaker: Anna Gallo (PhD student at the IMT School for Advanced Studies Lucca)