mlsanityData to 7 Oct 2026

Lov'asz Meets Weisfeiler and Leman

arXiv 1802.08876 ↗First posted 24 Feb 2018cs.DS math.COcited by 26

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Abstract (arXiv)

In this paper, we relate a beautiful theory by Lov'asz with a popular heuristic algorithm for the graph isomorphism problem, namely the color refinement algorithm and its k-dimensional generalization known as the Weisfeiler-Leman algorithm. We prove that two graphs G and H are indistinguishable by the color refinement algorithm if and only if, for all trees T, the number Hom(T,G) of homomorphisms from T to G equals the corresponding number Hom(T,H) for H. There is a natural system of linear equations whose nonnegative integer solutions correspond to the isomorphisms between two graphs. The nonnegative real solutions to this system are called fractional isomorphisms, and two graphs are fractionally isomorphic if and only if the color refinement algorithm cannot distinguish them (Tinhofer 1986, 1991). We show that, if we drop the nonnegativity constraints, that is, if we look for arbitrary real solutions, then a solution to the linear system exists if and only if, for all t, the two graphs have the same number of length-t walks. We lift the results for trees to an equivalence between numbers of homomorphisms from graphs of tree width k, the k-dimensional Weisfeiler-Leman algorithm, and the level-k Sherali-Adams relaxation of our linear program. We also obtain a partial result for graphs of bounded path width and solutions to our system where we drop the nonnegativity constraints. A consequence of our results is a quasi-linear time algorithm to decide whether, for two given graphs G and H, there is a tree T with Hom(T,G) = Hom(T,H).

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Feb 2018.

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Citing papers per year: 2021: 7; 2022: 2; 2023: 7; 2024: 5; 2025: 4; 2026: 1.

26 papers.

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  1. 2021Weisfeiler and Leman go Machine Learning: The Story so farcited by 121
  2. 2021The Logic of Graph Neural Networkscited by 84
  3. 2021Graph Neural Networks with Local Graph Parameterscited by 51
  4. 2023Equivariant Polynomials for Graph Neural Networkscited by 38
  5. 2022SpeqNets: Sparsity-aware Permutation-equivariant Graph Networkscited by 37
  6. 2023WL meet VCcited by 30
  7. 2023Fine-grained Expressivity of Graph Neural Networkscited by 25
  8. 2021The Power of the Weisfeiler-Leman Algorithm for Machine Learning with Graphscited by 19
  9. 2024Towards Bridging Generalization and Expressivity of Graph Neural Networkscited by 12
  10. 2023Expectation-Complete Graph Representations with Homomorphismscited by 10
  11. 2021A Generalized Weisfeiler-Lehman Graph Kernelcited by 9
  12. 2021On the approximation capability of GNNs in node classification/regression taskscited by 9
14 more
  1. 2022Weisfeiler and Leman Go Walking: Random Walk Kernels Revisitedcited by 8
  2. 2023On the Power of the Weisfeiler-Leman Test for Graph Motif Parameterscited by 6
  3. 2025Homomorphism Expressivity of Spectral Invariant Graph Neural Networkscited by 4
  4. 2025Graph Representational Learning: When Does More Expressivity Hurt Generalization?cited by 4
  5. 2023Structural Node Embeddings with Homomorphism Countscited by 3
  6. 2024Weisfeiler and Leman Go Loopy: A New Hierarchy for Graph Representational Learningcited by 2
  7. 2024The Role of Fibration Symmetries in Geometric Deep Learningcited by 2
  8. 2021Scaling up graph homomorphism for classification via samplingcited by 0
  9. 2023Local Fragments, Global Gains: Subgraph Counting using Graph Neural Networkscited by 0
  10. 2024Generalization of Graph Neural Networks through the Lens of Homomorphismcited by 0
  11. 2024Fine-Grained Expressive Power of Weisfeiler-Leman: A Homomorphism Counting Perspectivecited by 0
  12. 2025Caterpillar GNN: Replacing Message Passing with Efficient Aggregationcited by 0
  13. 2025Message Passing on the Edge: Towards Scalable and Expressive GNNscited by 0
  14. 2026The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networkscited by 0