Multiplexity amplifies geometry in networks

Many real-world networks 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 number 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.

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Publication Details

Journal
Physical Review Research
Published
2025-10-31
DOI
https://doi.org/10.1103/cm34-kjd4
Primary Topic
Complex Network Analysis Techniques
Type
article
Field-Weighted Citation Impact
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article

Multiplexity amplifies geometry in networks

Jasper van der Kolk, Marián Boguñá, Dmitri Krioukov, M. Ángeles Serrano
Physical Review Research
Complex Network Analysis Techniques
article

Multiplexity amplifies geometry in networks

Jasper van der Kolk, Marián Boguñá, Dmitri Krioukov, M. Ángeles Serrano
article en

Abstract

Many real-world networks 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 number 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.

Physical Review ResearchVol. 7(4)
Institució Catalana de Recerca i Estudis Avançats (ES), Northeastern University (US), Universitat de Barcelona (ES), Universidad del Noreste (MX)
Openalex Percentile: Top 70%
Complex Network Analysis Techniques
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