DeepAI

# Densest Subgraphs of a Dense Erdös-Rényi Graph. Asymptotics, Landscape and Universality

We consider the problem of estimating the edge density of densest K-node subgraphs of an Erdös-Rényi graph 𝔾(n,1/2). The problem is well-understood in the regime K=Θ(log n) and in the regime K=Θ(n). In the former case it can be reduced to the problem of estimating the size of largest cliques, and its extensions. In the latter case the full answer is known up to the order n^3 2 using sophisticated methods from the theory of spin glasses. The intermediate case K=n^α, α∈ (0,1) however is not well studied and this is our focus. We establish that that in this regime the density (that is the maximum number of edges supported by any K-node subgraph) is 1 4K^2+1+o(1) 2K^3 2√(log (n/K)), w.h.p. as n→∞, and provide more refined asymptotics under the o(·), for various ranges of α. This extends earlier similar results where this asymptotics was confirmed only when α is a small constant. We extend our results to the case of ”weighted” graphs, when the weights have either Gaussian or arbitrary sub-Gaussian distributions. The proofs are based on the second moment method combined with concentration bounds, the Borell-TIS inequality for the Gaussian case and the Talagrand's inequality for the case of distributions with bounded support (including the 𝔾(n,1/2) case). The case of general distribution is treated using a novel symmetrized version of the Lindeberg argument, which reduces the general case to the Gaussian case. Finally, using the results above we conduct the landscape analysis of the related Hidden Clique Problem, and establish that it exhibits an overlap gap property when the size of the clique is O(n^2 3), confirming a hypothesis stated in a previous related work.

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