Latent geometry emerging from network-driven processes

TL;DR

This paper explores how latent geometric structures emerge from network-driven processes, emphasizing generative models that reconstruct network geometry at various temporal scales to understand functional organization across different types of networks.

Contribution

It distinguishes between fixed-time and multi-scale generative modeling approaches, highlighting their roles in revealing emergent latent geometry in complex networks.

Findings

Latent geometry captures low-dimensional structures governing network dynamics.

Generative models reveal functional organization in biological, social, and technological networks.

Multi-scale approaches integrate dynamics across different temporal regimes.

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.