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Cited by 270

The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.

Graph Nonisomorphism Has Subexponential Size Proofs Unless the Polynomial-Time Hierarchy Collapses

Open access
Klivans, Adam R., van Melkebeek, Dieter·2002·SIAM Journal on Computing

Abstract

Traditional hardness versus randomness results focus on time-efficient randomized decision procedures. We generalize these trade-offs to a much wider class of randomized processes. We work out various applications, most notably to derandomizing Arthur-Merlin games. We show that every language with a bounded round Arthur-Merlin game has subexponential size membership proofs for infinitely many input lengths unless exponential time coincides with the third level of the polynomial-time hierarchy (and hence the polynomial-time hierarchy collapses). Since the graph nonisomorphism problem has a bounded round Arthur-Merlin game, this provides the first strong evidence that graph nonisomorphism has subexponential size proofs. We also establish hardness versus randomness trade-offs for space bounded computation.

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Metadata

Authors
Klivans, A. R., & van Melkebeek, D.
Venue
SIAM Journal on Computing
Published
2002-01
Volume
31
Issue
5
Pages
1501-1526
ISSN
  • 0097-5397 (ISSN-L)
  • 1095-7111
OpenAlex
W1558847040
Cited by
270
References
33
FWCI
11.43
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Graph Nonisomorphism Has Subexponential Size Proofs Unless the Polynomial-Time Hierarchy Collapses. https://app.cochat.ai/papers/10.1137/s0097539700389652
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Klivans, A. R., & van Melkebeek, D. (2002). Graph Nonisomorphism Has Subexponential Size Proofs Unless the Polynomial-Time Hierarchy Collapses. SIAM Journal on Computing. https://doi.org/10.1137/s0097539700389652

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.