Multiplexity amplifies geometry in networks
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866914342690095104 |
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| author | van der Kolk, Jasper Krioukov, Dmitri Boguñá, Marián Serrano, M. Ángeles |
| author_facet | van der Kolk, Jasper Krioukov, Dmitri Boguñá, Marián Serrano, M. Ángeles |
| contents | Many real-world network are multilayer, with nontrivial correlations across layers. Here we show that these correlations amplify geometry in networks. We focus on mutual clustering--a measure of the amount of triangles that are present in all layers among the same triplets of nodes--and find that this clustering is abnormally high in many real-world networks, even when clustering in each individual layer is weak. We explain this unexpected phenomenon using a simple multiplex network model with latent geometry: links that are most congruent with this geometry are the ones that persist across layers, amplifying the cross-layer triangle overlap. This result reveals a different dimension in which multilayer networks are radically distinct from their constituent layers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_17688 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multiplexity amplifies geometry in networks van der Kolk, Jasper Krioukov, Dmitri Boguñá, Marián Serrano, M. Ángeles Physics and Society Disordered Systems and Neural Networks Many real-world network are multilayer, with nontrivial correlations across layers. Here we show that these correlations amplify geometry in networks. We focus on mutual clustering--a measure of the amount of triangles that are present in all layers among the same triplets of nodes--and find that this clustering is abnormally high in many real-world networks, even when clustering in each individual layer is weak. We explain this unexpected phenomenon using a simple multiplex network model with latent geometry: links that are most congruent with this geometry are the ones that persist across layers, amplifying the cross-layer triangle overlap. This result reveals a different dimension in which multilayer networks are radically distinct from their constituent layers. |
| title | Multiplexity amplifies geometry in networks |
| topic | Physics and Society Disordered Systems and Neural Networks |
| url | https://arxiv.org/abs/2505.17688 |