English

The birth of geometry in exponential random graphs

Disordered Systems and Neural Networks 2021-10-01 v3 General Relativity and Quantum Cosmology High Energy Physics - Theory Mathematical Physics math.MP Probability

Abstract

Inspired by the prospect of having discretized spaces emerge from random graphs, we construct a collection of simple and explicit exponential random graph models that enjoy, in an appropriate parameter regime, a roughly constant vertex degree and form very large numbers of simple polygons (triangles or squares). The models avoid the collapse phenomena that plague naive graph Hamiltonians based on triangle or square counts. More than that, statistically significant numbers of other geometric primitives (small pieces of regular lattices, cubes) emerge in our ensemble, even though they are not in any way explicitly pre-programmed into the formulation of the graph Hamiltonian, which only depends on properties of paths of length 2. While much of our motivation comes from hopes to construct a graph-based theory of random geometry (Euclidean quantum gravity), our presentation is completely self-contained within the context of exponential random graph theory, and the range of potential applications is considerably more broad.

Keywords

Cite

@article{arxiv.2102.11477,
  title  = {The birth of geometry in exponential random graphs},
  author = {Pawat Akara-pipattana and Thiparat Chotibut and Oleg Evnin},
  journal= {arXiv preprint arXiv:2102.11477},
  year   = {2021}
}

Comments

v3: further clarifications and references added; accepted for publication

R2 v1 2026-06-23T23:25:38.596Z