Testing Graphs against an Unknown Distribution
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.
 Publication:

arXiv eprints
 Pub Date:
 May 2019
 arXiv:
 arXiv:1905.09903
 Bibcode:
 2019arXiv190509903G
 Keywords:

 Mathematics  Combinatorics;
 Computer Science  Discrete Mathematics
 EPrint:
 A short version of this paper appeared in the Proceedings of STOC '19