# Anticoncentration for subgraph counts in random graphs

## TL;DR

This paper establishes near-optimal bounds on the probability that the count of a fixed subgraph in a random graph falls into a specific small value, advancing understanding of small-ball probabilities in random graph theory.

## Contribution

It introduces a novel anticoncentration inequality for almost linear random vectors and applies it to bound subgraph count probabilities in Erdős–Rényi graphs.

## Key findings

- Proves that for connected graphs, the probability of exactly x copies is at most n^{1-v(H)+o(1)}.
- Develops a new anticoncentration inequality for vectors with near-linear behavior.
- Provides a method to analyze small-ball probabilities for subgraph counts.

## Abstract

Fix a graph $H$ and some $p\in (0,1)$, and let $X_H$ be the number of copies of $H$ in a random graph $G(n,p)$. Random variables of this form have been intensively studied since the foundational work of Erd\H{o}s and R\'{e}nyi. There has been a great deal of progress over the years on the large-scale behaviour of $X_H$, but the more challenging problem of understanding the small-ball probabilities has remained poorly understood until now. More precisely, how likely can it be that $X_H$ falls in some small interval or is equal to some particular value? In this paper we prove the almost-optimal result that if $H$ is connected then for any $x\in \mathbb{N}$ we have $\Pr(X_H=x)\le n^{1-v(H)+o(1)}$. Our proof proceeds by iteratively breaking $X_H$ into different components which fluctuate at "different scales", and relies on a new anticoncentration inequality for random vectors that behave "almost linearly".