Uniform Reliability for Unbounded Homomorphism-Closed Graph Queries

Author Antoine Amarilli

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Antoine Amarilli
  • LTCI, Télécom Paris, Institut Polytechnique de Paris, France


I am grateful to Mikaël Monet, Charles Paperman, and Martin Retaux for helpful discussions about this research. Thanks to the reviewers for their helpful feedback.

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Antoine Amarilli. Uniform Reliability for Unbounded Homomorphism-Closed Graph Queries. In 26th International Conference on Database Theory (ICDT 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 255, pp. 14:1-14:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)


We study the uniform query reliability problem, which asks, for a fixed Boolean query Q, given an instance I, how many subinstances of I satisfy Q. Equivalently, this is a restricted case of Boolean query evaluation on tuple-independent probabilistic databases where all facts must have probability 1/2. We focus on graph signatures, and on queries closed under homomorphisms. We show that for any such query that is unbounded, i.e., not equivalent to a union of conjunctive queries, the uniform reliability problem is #P-hard. This recaptures the hardness, e.g., of s-t connectedness, which counts how many subgraphs of an input graph have a path between a source and a sink. This new hardness result on uniform reliability strengthens our earlier hardness result on probabilistic query evaluation for unbounded homomorphism-closed queries [Amarilli and Ceylan, 2021]. Indeed, our earlier proof crucially used facts with probability 1, so it did not apply to the unweighted case. The new proof presented in this paper avoids this; it uses our recent hardness result on uniform reliability for non-hierarchical conjunctive queries without self-joins [Antoine Amarilli and Benny Kimelfeld, 2022], along with new techniques.

Subject Classification

ACM Subject Classification
  • Theory of computation → Database query processing and optimization (theory)
  • Uniform reliability
  • #P-hardness
  • probabilistic databases


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