# Testing Graphs against an Unknown Distribution

## TL;DR

This paper characterizes which graph properties remain testable when the vertex distribution is unknown and arbitrary, extending classical graph property testing to a more general and realistic setting.

## Contribution

The paper provides a complete characterization of testable graph properties under unknown vertex distributions, including a new removal lemma for vertex-weighted graphs.

## Key findings

- Characterization of testable properties under unknown distributions
- A new removal lemma for vertex-weighted graphs
- Extension of classical graph testing models

## Abstract

The area of graph property testing seeks to understand the relation between the global properties of a graph and its local statistics. In the classical model, the local statistics of a graph is defined relative to a uniform distribution over the graph's vertex set. A graph property $\mathcal{P}$ is said to be testable if the local statistics of a graph can allow one to distinguish between graphs satisfying $\mathcal{P}$ and those that are far from satisfying it. Goldreich recently introduced a generalization of this model in which one endows the vertex set of the input graph with an arbitrary and unknown distribution, and asked which of the properties that can be tested in the classical model can also be tested in this more general setting. We completely resolve this problem by giving a (surprisingly "clean") characterization of these properties. To this end, we prove a removal lemma for vertex weighted graphs which is of independent interest.