The Iteration Number of Colour Refinement
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Abstract (arXiv)
The Colour Refinement procedure and its generalisation to higher dimensions, the Weisfeiler-Leman algorithm, are central subroutines in approaches to the graph isomorphism problem. In an iterative fashion, Colour Refinement computes a colouring of the vertices of its input graph. A trivial upper bound on the iteration number of Colour Refinement on graphs of order n is n-1. We show that this bound is tight. More precisely, we prove via explicit constructions that there are infinitely many graphs G on which Colour Refinement takes |G|-1 iterations to stabilise. Modifying the infinite families that we present, we show that for every natural number n >= 10, there are graphs on n vertices on which Colour Refinement requires at least n-2 iterations to reach stabilisation.
Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 May 2020.
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Citing papers per year: 2021: 3; 2022: 3; 2023: 2; 2024: 3; 2025: 1.
12 papers.
- 2021Weisfeiler and Leman go Machine Learning: The Story so farcited by 121
- 2022SpeqNets: Sparsity-aware Permutation-equivariant Graph Networkscited by 37
- 2023WL meet VCcited by 30
- 2023Fine-grained Expressivity of Graph Neural Networkscited by 25
- 2021The Power of the Weisfeiler-Leman Algorithm for Machine Learning with Graphscited by 19
- 2024Probabilistic Graph Rewiring via Virtual Nodescited by 15
- 2022Weisfeiler-Lehman goes Dynamic: An Analysis of the Expressive Power of Graph Neural Networks for Attributed and Dynamic Graphscited by 12
- 2024Towards Bridging Generalization and Expressivity of Graph Neural Networkscited by 12
- 2021On the approximation capability of GNNs in node classification/regression taskscited by 9
- 2022Gradual Weisfeiler-Leman: Slow and Steady Wins the Racecited by 5
- 2024How Hard Is It for Message-Passing GNNs to Simulate One Weisfeiler-Lehman Color-Refinement Step?cited by 2
- 2025A method for the systematic generation of graph XAI benchmarks via Weisfeiler-Leman coloringcited by 0
- 2025A method for the systematic generation of graph XAI benchmarks via Weisfeiler-Leman coloring18 May 2025
- 2024Towards Bridging Generalization and Expressivity of Graph Neural Networks14 Oct 2024
- 2024How Hard Is It for Message-Passing GNNs to Simulate One Weisfeiler-Lehman Color-Refinement Step?2 Oct 2024
- 2024Probabilistic Graph Rewiring via Virtual Nodes27 May 2024
- 2023Fine-grained Expressivity of Graph Neural Networks6 Jun 2023
- 2023WL meet VC26 Jan 2023
- 2022Weisfeiler-Lehman goes Dynamic: An Analysis of the Expressive Power of Graph Neural Networks for Attributed and Dynamic Graphs8 Oct 2022
- 2022Gradual Weisfeiler-Leman: Slow and Steady Wins the Race19 Sep 2022
- 2022SpeqNets: Sparsity-aware Permutation-equivariant Graph Networks25 Mar 2022
- 2021Weisfeiler and Leman go Machine Learning: The Story so far18 Dec 2021
- 2021On the approximation capability of GNNs in node classification/regression tasks16 Jun 2021
- 2021The Power of the Weisfeiler-Leman Algorithm for Machine Learning with Graphs12 May 2021