On the Combinatorial Power of the Weisfeiler-Lehman Algorithm
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Abstract (arXiv)
The classical Weisfeiler-Lehman method WL[2] uses edge colors to produce a powerful graph invariant. It is at least as powerful in its ability to distinguish non-isomorphic graphs as the most prominent algebraic graph invariants. It determines not only the spectrum of a graph, and the angles between standard basis vectors and the eigenspaces, but even the angles between projections of standard basis vectors into the eigenspaces. Here, we investigate the combinatorial power of WL[2]. For sufficiently large k, WL[k] determines all combinatorial properties of a graph. Many traditionally used combinatorial invariants are determined by WL[k] for small k. We focus on two fundamental invariants, the num- ber of cycles Cp of length p, and the number of cliques Kp of size p. We show that WL[2] determines the number of cycles of lengths up to 6, but not those of length 8. Also, WL[2] does not determine the number of 4-cliques.
Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Apr 2017.
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Citing papers per year: 2021: 3; 2022: 2; 2023: 7; 2024: 5; 2025: 1.
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- 2021Topological Graph Neural Networkscited by 86
- 2022Boosting the Cycle Counting Power of Graph Neural Networks with I2-GNNscited by 55
- 2021Graph Neural Networks with Local Graph Parameterscited by 51
- 2022SpeqNets: Sparsity-aware Permutation-equivariant Graph Networkscited by 37
- 2023WL meet VCcited by 30
- 2024Cometh: A continuous-time discrete-state graph diffusion modelcited by 29
- 2023Fine-grained Expressivity of Graph Neural Networkscited by 25
- 2021The Power of the Weisfeiler-Leman Algorithm for Machine Learning with Graphscited by 19
- 2023An Efficient Subgraph GNN with Provable Substructure Counting Powercited by 13
- 2023Distance-Restricted Folklore Weisfeiler-Leman GNNs with Provable Cycle Counting Powercited by 11
- 2023On the Power of the Weisfeiler-Leman Test for Graph Motif Parameterscited by 6
- 2024GEFL: Extended Filtration Learning for Graph Classificationcited by 5
6 more
- 2024Graph as Point Setcited by 4
- 2025Homomorphism Expressivity of Spectral Invariant Graph Neural Networkscited by 4
- 2024Foundations and Frontiers of Graph Learning Theorycited by 3
- 2023Technical report: Graph Neural Networks go Grammaticalcited by 0
- 2023Local Fragments, Global Gains: Subgraph Counting using Graph Neural Networkscited by 0
- 2024Finding path and cycle counting formulae in graphs with Deep Reinforcement Learningcited by 0
- 2025Homomorphism Expressivity of Spectral Invariant Graph Neural Networks1 Mar 2025
- 2024Finding path and cycle counting formulae in graphs with Deep Reinforcement Learning2 Oct 2024
- 2024Foundations and Frontiers of Graph Learning Theory3 Jul 2024
- 2024Cometh: A continuous-time discrete-state graph diffusion model10 Jun 2024
- 2024GEFL: Extended Filtration Learning for Graph Classification4 Jun 2024
- 2024Graph as Point Set5 May 2024
- 2023On the Power of the Weisfeiler-Leman Test for Graph Motif Parameters29 Sep 2023
- 2023Distance-Restricted Folklore Weisfeiler-Leman GNNs with Provable Cycle Counting Power10 Sep 2023
- 2023Fine-grained Expressivity of Graph Neural Networks6 Jun 2023
- 2023Local Fragments, Global Gains: Subgraph Counting using Graph Neural Networks31 May 2023
- 2023An Efficient Subgraph GNN with Provable Substructure Counting Power19 Mar 2023
- 2023Technical report: Graph Neural Networks go Grammatical2 Mar 2023
6 more
- 2023WL meet VC26 Jan 2023
- 2022Boosting the Cycle Counting Power of Graph Neural Networks with I2-GNNs22 Oct 2022
- 2022SpeqNets: Sparsity-aware Permutation-equivariant Graph Networks25 Mar 2022
- 2021Graph Neural Networks with Local Graph Parameters12 Jun 2021
- 2021The Power of the Weisfeiler-Leman Algorithm for Machine Learning with Graphs12 May 2021
- 2021Topological Graph Neural Networks15 Feb 2021
- 2021Topological Graph Neural Networkscited by 86
- 2022Boosting the Cycle Counting Power of Graph Neural Networks with I2-GNNscited by 55
- 2021Graph Neural Networks with Local Graph Parameterscited by 51
- 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
- 2023An Efficient Subgraph GNN with Provable Substructure Counting Powercited by 13
- 2023Distance-Restricted Folklore Weisfeiler-Leman GNNs with Provable Cycle Counting Powercited by 11
- 2023On the Power of the Weisfeiler-Leman Test for Graph Motif Parameterscited by 6
- 2024GEFL: Extended Filtration Learning for Graph Classificationcited by 5
- 2024Graph as Point Setcited by 4
12 most cited of 17 All 17 (JSON)
- 2025Homomorphism Expressivity of Spectral Invariant Graph Neural Networks1 Mar 2025
- 2024Finding path and cycle counting formulae in graphs with Deep Reinforcement Learning2 Oct 2024
- 2024Foundations and Frontiers of Graph Learning Theory3 Jul 2024
- 2024GEFL: Extended Filtration Learning for Graph Classification4 Jun 2024
- 2024Graph as Point Set5 May 2024
- 2023On the Power of the Weisfeiler-Leman Test for Graph Motif Parameters29 Sep 2023
- 2023Distance-Restricted Folklore Weisfeiler-Leman GNNs with Provable Cycle Counting Power10 Sep 2023
- 2023Fine-grained Expressivity of Graph Neural Networks6 Jun 2023
- 2023Local Fragments, Global Gains: Subgraph Counting using Graph Neural Networks31 May 2023
- 2023An Efficient Subgraph GNN with Provable Substructure Counting Power19 Mar 2023
- 2023Technical report: Graph Neural Networks go Grammatical2 Mar 2023
- 2023WL meet VC26 Jan 2023
12 newest of 17 All 17 (JSON)