math.COAug 31, 2026

Exponential random graph models with soft clique constraints

Authors: Yasmin Tousinejad, Vera Koponen

Abstract

Let r≥3r\geq3 be fixed, and let Gn\mathbf{G}_n be the set of all simple graphs with vertex set [n]={1,…,n}[n]=\{1,\ldots,n\}. We consider an exponential random graph model which gives higher probability to G∈GnG \in \mathbf{G}_n than to H∈GnH \in \mathbf{G}_n if GG has fewer rr-cliques than HH. But all graphs in Gn\mathbf{G}_n have positive probability. The degree to which graphs with fewer rr-cliques are given higher probability is determined by a positive weight ww. We prove that, asymptotically almost surely as n→∞n \to \infty, a random graph from Gn\mathbf{G}_n has a vertex partition into r−1r-1 parts of roughly equal size, the density of edges between the parts is close to 1/21/2, and for every ε>0\varepsilon > 0 the density of edges within any part is less than ε\varepsilon. The asymptotic structural properties are independent of the weight ww as long as it is positive. We also extend the result to the context of several clique sizes, each one with its own weight.

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