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Logarithmic Weisfeiler-Leman Identifies All Planar Graphs

Authors Martin Grohe , Sandra Kiefer

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LIPIcs.ICALP.2021.134.pdf
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ACM Subject Classification
  • Theory of computation → Finite Model Theory
  • Mathematics of computing → Graph theory
Keywords
  • Weisfeiler-Leman algorithm
  • finite-variable logic
  • isomorphism testing
  • planar graphs
  • quantifier depth
  • iteration number

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Abstract

The Weisfeiler-Leman (WL) algorithm is a well-known combinatorial procedure for detecting symmetries in graphs and it is widely used in graph-isomorphism tests. It proceeds by iteratively refining a colouring of vertex tuples. The number of iterations needed to obtain the final output is crucial for the parallelisability of the algorithm. 
We show that there is a constant k such that every planar graph can be identified (that is, distinguished from every non-isomorphic graph) by the k-dimensional WL algorithm within a logarithmic number of iterations. This generalises a result due to Verbitsky (STACS 2007), who proved the same for 3-connected planar graphs.
The number of iterations needed by the k-dimensional WL algorithm to identify a graph corresponds to the quantifier depth of a sentence that defines the graph in the (k+1)-variable fragment C^{k+1} of first-order logic with counting quantifiers. Thus, our result implies that every planar graph is definable with a C^{k+1}-sentence of logarithmic quantifier depth.

Cite As Get BibTex

Martin Grohe and Sandra Kiefer. Logarithmic Weisfeiler-Leman Identifies All Planar Graphs. In 48th International Colloquium on Automata, Languages, and Programming (ICALP 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 198, pp. 134:1-134:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021) https://doi.org/10.4230/LIPIcs.ICALP.2021.134

Author Details

Martin Grohe
  • RWTH Aachen University, Germany
Sandra Kiefer
  • University of Warsaw, Poland
  • RWTH Aachen University, Germany

Funding

  • Kiefer, Sandra: supported by the European Research Council under the European Union’s Horizon 2020 research and innovation programme (ERC consolidator grant LIPA, agreement no. 683080).

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