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Extracting the transitivity backbone of bipartite networks
Lucía S Ramirez1,2, Roya Aliakbarisani1,2, M Ángeles Serrano1,2,3
1Departament de Física de la Matèria Condensada, Universitat de Barcelona, Barcelona, Spain.
Nature Communications
|June 29, 2026
Summary
We developed a statistical filter to distinguish geometric structure from noise in bipartite networks. This method enhances understanding of network backbones and improves data analysis across various systems.
Area of Science:
- Network Science
- Statistical Modeling
- Complex Systems
Background:
- Real bipartite networks exhibit both random mixing and structured connectivity.
- Geometric network models effectively capture this balance.
- Distinguishing signal from noise is crucial for accurate network analysis.
Purpose of the Study:
- Introduce a statistical filter to classify nodes in bipartite networks.
- Separate geometric signal from degree-constrained noise.
- Improve inference of latent network parameters and network structure.
Main Methods:
- Developed a node-level bipartite clustering filter.
- Benchmarked clustering against degree-preserving randomizations.
- Classified nodes as geometric (signal) or noise.
Main Results:
- High classification accuracy in synthetic networks.
- Sharpened inference of latent geometric parameters.
- Isolated recurrent neighborhoods and removed noise in empirical systems (metabolism, online groups, plant-pollinator interactions, languages).
Conclusions:
- Filtering reveals a compact geometric backbone essential for network connectivity.
- The method preserves downstream classifier accuracy.
- Offers a scalable approach to disentangle structure from noise in bipartite networks.
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