Local limit theorems for subgraph counts
Ashwin Sah, Mehtaab Sawhney

TL;DR
This paper develops a comprehensive framework combining Fourier analysis, decoupling, and hypercontractivity to establish local limit theorems for subgraph counts in random graphs, addressing anticoncentration and providing counterexamples.
Contribution
It introduces new methods for local limit theorems in graph statistics, extends previous results, and clarifies the limitations of anticoncentration in certain graph models.
Findings
Established local CLT for connected subgraph counts in G(n,p)
Derived local limit theorem for induced subgraph counts with restrictions on p
Provided counterexamples showing anticoncentration fails in specific graph scenarios
Abstract
We introduce a general framework for studying anticoncentration and local limit theorems for random variables, including graph statistics. Our methods involve an interplay between Fourier analysis, decoupling, hypercontractivity of Boolean functions, and transference between ``fixed-size'' and ``independent'' models. We also adapt a notion of ``graph factors'' due to Janson. As a consequence, we derive a local central limit theorem for connected subgraph counts in the Erd\H{o}s-Renyi random graph , building on work of Gilmer and Kopparty and of Berkowitz. These results improve an anticoncentration result of Fox, Kwan, and Sauermann and partially answers a question of Fox, Kwan, and Sauermann. We also derive a local limit central limit theorem for induced subgraph counts, as long as is bounded away from a set of ``problematic'' densities, partially answering a question of…
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