Latent geometry emerging from network-driven processes
Physics and Society
2025-12-02 v1 Disordered Systems and Neural Networks
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.
Cite
@article{arxiv.2506.09616,
title = {Latent geometry emerging from network-driven processes},
author = {Andrea Filippo Beretta and Davide Zanchetta and Sebastiano Bontorin and Manlio De Domenico},
journal= {arXiv preprint arXiv:2506.09616},
year = {2025}
}