The Weisfeiler-Leman Dimension of Planar Graphs is at most 3
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Abstract (arXiv)
We prove that the Weisfeiler-Leman (WL) dimension of the class of all finite planar graphs is at most 3. In particular, every finite planar graph is definable in first-order logic with counting using at most 4 variables. The previously best known upper bounds for the dimension and number of variables were 14 and 15, respectively. First we show that, for dimension 3 and higher, the WL-algorithm correctly tests isomorphism of graphs in a minor-closed class whenever it determines the orbits of the automorphism group of any arc-colored 3-connected graph belonging to this class. Then we prove that, apart from several exceptional graphs (which have WL-dimension at most 2), the individualization of two correctly chosen vertices of a colored 3-connected planar graph followed by the 1-dimensional WL-algorithm produces the discrete vertex partition. This implies that the 3-dimensional WL-algorithm determines the orbits of a colored 3-connected planar graph. As a byproduct of the proof, we get a classification of the 3-connected planar graphs with fixing number 3.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Aug 2017.
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Citing papers per year: 2021: 3; 2022: 2; 2023: 4; 2024: 0; 2025: 0; 2026: 1.
10 papers.
- 2021Weisfeiler and Leman go Machine Learning: The Story so farcited by 121
- 2021The Logic of Graph Neural Networkscited by 84
- 2022SpeqNets: Sparsity-aware Permutation-equivariant Graph Networkscited by 37
- 2023WL meet VCcited by 30
- 2023Fine-grained Expressivity of Graph Neural Networkscited by 25
- 2022Weisfeiler and Leman Go Infinite: Spectral and Combinatorial Pre-Coloringscited by 15
- 2023PlanE: Representation Learning over Planar Graphscited by 11
- 2026On the Expressive Power of GNNs for Boolean Satisfiabilitycited by 1
- 2021A systematic approach to random data augmentation on graph neural networkscited by 0
- 2023Local Fragments, Global Gains: Subgraph Counting using Graph Neural Networkscited by 0
- 2026On the Expressive Power of GNNs for Boolean Satisfiability9 Feb 2026
- 2023PlanE: Representation Learning over Planar Graphs3 Jul 2023
- 2023Fine-grained Expressivity of Graph Neural Networks6 Jun 2023
- 2023Local Fragments, Global Gains: Subgraph Counting using Graph Neural Networks31 May 2023
- 2023WL meet VC26 Jan 2023
- 2022SpeqNets: Sparsity-aware Permutation-equivariant Graph Networks25 Mar 2022
- 2022Weisfeiler and Leman Go Infinite: Spectral and Combinatorial Pre-Colorings31 Jan 2022
- 2021Weisfeiler and Leman go Machine Learning: The Story so far18 Dec 2021
- 2021A systematic approach to random data augmentation on graph neural networks8 Dec 2021
- 2021The Logic of Graph Neural Networks29 Apr 2021