Random Algebraic Graphs and Their Convergence to Erdos-Renyi

TL;DR

This paper investigates when random algebraic graphs can be distinguished from Erdős-Rényi graphs, revealing geometric and algebraic conditions under which they are statistically and computationally similar or different.

Contribution

It provides new results on the distinguishability of random algebraic graphs from Erdős-Rényi, including Fourier-analytic techniques and evidence for an exponential statistical-computational gap.

Findings

For certain geometric models, the paper matches existing results for connection probabilities.

In algebraic models, it shows indistinguishability when group size is exponential in n.

Low-degree polynomial tests cannot distinguish graphs when group size is subexponential.

Abstract

A random algebraic graph is defined by a group $G$ with a uniform distribution over it and a connection $\sigma:G\longrightarrow[0,1]$ with expectation $p,$ satisfying $\sigma(g)=\sigma(g^{-1}).$ The random graph $\mathsf{RAG}(n,G,p,\sigma)$ with vertex set $[n]$ is formed as follows. First, $n$ independent vectors $x_1,\ldots,x_n$ are sampled uniformly from $G.$ Then, vertices $i,j$ are connected with probability $\sigma(x_ix_j^{-1}).$ This model captures random geometric graphs over the sphere and the hypercube, certain regimes of the stochastic block model, and random subgraphs of Cayley graphs. The main question of interest to the current paper is: when is a random algebraic graph statistically and/or computationally distinguishable from $\mathsf{G}(n,p)$? Our results fall into two categories. 1) Geometric. We focus on the case $G =\{\pm1\}^d$ and use Fourier-analytic tools. For hard threshold connections, we match [LMSY22b] for $p = \omega(1/n)$ and for $1/(r\sqrt{d})$-Lipschitz connections we extend the results of [LR21b] when $d = \Omega(n\log n)$ to the non-monotone setting. We study other connections such as indicators of interval unions and low-degree polynomials. 2) Algebraic. We provide evidence for an exponential statistical-computational gap. Consider any finite group $G$ and let $A\subseteq G$ be a set of elements formed by including each set of the form $\{g, g^{-1}\}$ independently with probability $1/2.$ Let $\Gamma_n(G,A)$ be the distribution of random graphs formed by taking a uniformly random induced subgraph of size $n$ of the Cayley graph $\Gamma(G,A).$ Then, $\Gamma_n(G,A)$ and $\mathsf{G}(n,1/2)$ are statistically indistinguishable with high probability over $A$ if and only if $\log|G|\gtrsim n.$ However, low-degree polynomial tests fail to distinguish $\Gamma_n(G,A)$ and $\mathsf{G}(n,1/2)$ with high probability over $A$ when $\log |G|=\log^{\Omega(1)}n.$