Testing Versus Estimation of Graph Properties, Revisited

Authors Lior Gishboliner, Nick Kushnir, Asaf Shapira

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Lior Gishboliner
  • ETH Zürich, Switzerland
Nick Kushnir
  • School of Mathematics, Tel Aviv University, Israel
Asaf Shapira
  • School of Mathematics, Tel Aviv University, Israel

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Lior Gishboliner, Nick Kushnir, and Asaf Shapira. Testing Versus Estimation of Graph Properties, Revisited. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 275, pp. 46:1-46:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)


A graph G on n vertices is ε-far from property P if one should add/delete at least ε n² edges to turn G into a graph satisfying P. A distance estimator for P is an algorithm that given G and α, ε > 0 distinguishes between the case that G is (α-ε)-close to 𝒫 and the case that G is α-far from 𝒫. If P has a distance estimator whose query complexity depends only on ε, then P is said to be estimable. Every estimable property is clearly also testable, since testing corresponds to estimating with α = ε. A central result in the area of property testing is the Fischer-Newman theorem, stating that an inverse statement also holds, that is, that every testable property is in fact estimable. The proof of Fischer and Newmann was highly ineffective, since it incurred a tower-type loss when transforming a testing algorithm for P into a distance estimator. This raised the natural problem, studied recently by Fiat-Ron and by Hoppen-Kohayakawa-Lang-Lefmann-Stagni, whether one can find a transformation with a polynomial loss. We obtain the following results. - We show that if P is hereditary, then one can turn a tester for P into a distance estimator with an exponential loss. This is an exponential improvement over the result of Hoppen et. al., who obtained a transformation with a double exponential loss. - We show that for every P, one can turn a testing algorithm for P into a distance estimator with a double exponential loss. This improves over the transformation of Fischer-Newman that incurred a tower-type loss. Our main conceptual contribution in this work is that we manage to turn the approach of Fischer-Newman, which was inherently ineffective, into an efficient one. On the technical level, our main contribution is in establishing certain properties of Frieze-Kannan Weak Regular partitions that are of independent interest.

Subject Classification

ACM Subject Classification
  • Mathematics of computing → Approximation algorithms
  • Testing
  • estimation
  • weak regularity
  • randomized algorithms
  • graph theory
  • Frieze-Kannan Regularity


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