Extracting the geometric backbone of bipartite networks
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866916970469785600 |
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| author | Ramírez, Lucía S. Aliakbarisani, Roya Serrano, M. Ángeles Boguñá, Marián |
| author_facet | Ramírez, Lucía S. Aliakbarisani, Roya Serrano, M. Ángeles Boguñá, Marián |
| contents | Real bipartite networks combine degree-constrained random mixing with structured, locality-like rules. We introduce a statistical filter that benchmarks node-level bipartite clustering against degree-preserving randomizations to classify nodes as geometric (signal) or random-like (noise). In synthetic mixtures with known ground truth, the filter achieves high F-scores and sharpens inference of latent geometric parameters. Applied to four empirical systems -- metabolism, online group membership, plant-pollinator interactions, and languages -- it isolates recurrent neighborhoods while removing ubiquitous or weakly co-occurring entities. Filtering exposes a compact geometric backbone that disproportionately sustains connectivity under percolation and preserves downstream classifier accuracy in node-feature tasks, offering a simple, scalable way to disentangle structure from noise in bipartite networks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_18726 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Extracting the geometric backbone of bipartite networks Ramírez, Lucía S. Aliakbarisani, Roya Serrano, M. Ángeles Boguñá, Marián Physics and Society Data Analysis, Statistics and Probability Real bipartite networks combine degree-constrained random mixing with structured, locality-like rules. We introduce a statistical filter that benchmarks node-level bipartite clustering against degree-preserving randomizations to classify nodes as geometric (signal) or random-like (noise). In synthetic mixtures with known ground truth, the filter achieves high F-scores and sharpens inference of latent geometric parameters. Applied to four empirical systems -- metabolism, online group membership, plant-pollinator interactions, and languages -- it isolates recurrent neighborhoods while removing ubiquitous or weakly co-occurring entities. Filtering exposes a compact geometric backbone that disproportionately sustains connectivity under percolation and preserves downstream classifier accuracy in node-feature tasks, offering a simple, scalable way to disentangle structure from noise in bipartite networks. |
| title | Extracting the geometric backbone of bipartite networks |
| topic | Physics and Society Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2509.18726 |