mlsanityData to 7 Oct 2026

The Power of the Weisfeiler-Leman Algorithm for Machine Learning with Graphs

arXiv 2105.05911 ↗First posted 12 May 2021cs.LG cs.AI cs.DScited by 19

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Abstract (arXiv)

In recent years, algorithms and neural architectures based on the Weisfeiler-Leman algorithm, a well-known heuristic for the graph isomorphism problem, emerged as a powerful tool for (supervised) machine learning with graphs and relational data. Here, we give a comprehensive overview of the algorithm's use in a machine learning setting. We discuss the theoretical background, show how to use it for supervised graph- and node classification, discuss recent extensions, and its connection to neural architectures. Moreover, we give an overview of current applications and future directions to stimulate research.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). Builds on 62 of its 106 references that are in the map. First posted on arXiv 12 May 2021.

Builds on

62 of 106 references.

References · 62

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38 more
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50 most cited of 62 All 62 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 4; 2023: 3; 2024: 4; 2025: 4; 2026: 1.

19 papers.

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  1. 2021Reconstruction for Powerful Graph Representationscited by 65
  2. 2023Fine-grained Expressivity of Graph Neural Networkscited by 25
  3. 2022Exponentially Improving the Complexity of Simulating the Weisfeiler-Lehman Test with Graph Neural Networkscited by 20
  4. 2021Graph Filtration Kernelscited by 8
  5. 2022Weisfeiler and Leman Go Walking: Random Walk Kernels Revisitedcited by 8
  6. 2024Sparse Covariance Neural Networkscited by 6
  7. 2022Gradual Weisfeiler-Leman: Slow and Steady Wins the Racecited by 5
  8. 2024Graph Neural Networks and Arithmetic Circuitscited by 3
  9. 2025On the Relationship Between Robustness and Expressivity of Graph Neural Networkscited by 3
  10. 2022Literature Review: Graph Kernels in Chemoinformaticscited by 1
  11. 2024Explaining the Power of Topological Data Analysis in Graph Machine Learningcited by 1
  12. 2025MILP-SAT-GNN: Yet Another Neural SAT Solvercited by 1
7 more
  1. 2021A systematic approach to random data augmentation on graph neural networkscited by 0
  2. 2023Attacking Graph Neural Networks with Bit Flips: Weisfeiler and Lehman Go Indifferentcited by 0
  3. 2023Concept Prerequisite Relation Prediction by Using Permutation-Equivariant Directed Graph Neural Networkscited by 0
  4. 2024The μG Language for Programming Graph Neural Networkscited by 0
  5. 2025Weisfeiler and Leman Go Gambling: Why Expressive Lottery Tickets Wincited by 0
  6. 2025GoMS: Graph of Molecule Substructure Network for Molecule Property Predictioncited by 0
  7. 2026A Unifying Relational Perspective on Expressive Lottery Ticketscited by 0