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arXiv · 2506.09616

Latent geometry emerging from network-driven processes

Andrea Filippo Beretta·Davide Zanchetta·Sebastiano Bontorin·Manlio De Domenico
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Abstract

Understanding network functionality requires integrating structure and dynamics, and emergent latent geometry induced by network-driven processes captures the low-dimensional spaces governing this interplay. Here, we focus on generative-model-based approaches, distinguishing two reconstruction classes: fixed-time methods, which infer geometry at specific temporal scales (e.g., equilibrium), and multi-scale methods, which integrate dynamics across near- and far-from-equilibrium scales. Over the past decade, these models have revealed functional organization in biological, social, and technological networks.

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Andrea Filippo Beretta, Davide Zanchetta, Sebastiano Bontorin, Manlio De Domenico. 2025-06-11. Latent geometry emerging from network-driven processes. https://doi.org/10.1038/s44260-025-00063-x

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