mlsanityData to 7 Oct 2026

Graph Homomorphism Convolution

arXiv 2005.01214 ↗First posted 3 May 2020cs.LG cs.DM math.COcited by 27

Not on the map. Why?

Abstract (arXiv)

In this paper, we study the graph classification problem from the graph homomorphism perspective. We consider the homomorphisms from F to G, where G is a graph of interest (e.g. molecules or social networks) and F belongs to some family of graphs (e.g. paths or non-isomorphic trees). We show that graph homomorphism numbers provide a natural invariant (isomorphism invariant and F-invariant) embedding maps which can be used for graph classification. Viewing the expressive power of a graph classifier by the F-indistinguishable concept, we prove the universality property of graph homomorphism vectors in approximating F-invariant functions. In practice, by choosing F whose elements have bounded tree-width, we show that the homomorphism method is efficient compared with other methods.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 May 2020.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 6; 2022: 6; 2023: 5; 2024: 6; 2025: 3; 2026: 1.

27 papers.

Sort:

  1. 2021Weisfeiler and Leman go Machine Learning: The Story so farcited by 121
  2. 2022Ordered Subgraph Aggregation Networkscited by 57
  3. 2022Provably expressive temporal graph networkscited by 56
  4. 2021Graph Neural Networks with Local Graph Parameterscited by 51
  5. 2022KerGNNs: Interpretable Graph Neural Networks with Graph Kernelscited by 50
  6. 2022SpeqNets: Sparsity-aware Permutation-equivariant Graph Networkscited by 37
  7. 2023WL meet VCcited by 30
  8. 2023Fine-grained Expressivity of Graph Neural Networkscited by 25
  9. 2022Exponentially Improving the Complexity of Simulating the Weisfeiler-Lehman Test with Graph Neural Networkscited by 20
  10. 2021The Power of the Weisfeiler-Leman Algorithm for Machine Learning with Graphscited by 19
  11. 2024Homomorphism Counts for Graph Neural Networks: All About That Basiscited by 16
  12. 2024Weisfeiler-Leman at the margin: When more expressivity matterscited by 13
15 more
  1. 2024Towards Bridging Generalization and Expressivity of Graph Neural Networkscited by 12
  2. 2023Expectation-Complete Graph Representations with Homomorphismscited by 10
  3. 2022Weisfeiler and Leman Go Walking: Random Walk Kernels Revisitedcited by 8
  4. 2024Homomorphism Counts as Structural Encodings for Graph Learningcited by 6
  5. 2021Equivariant and Invariant Reynolds Networkscited by 4
  6. 2025Graph Representational Learning: When Does More Expressivity Hurt Generalization?cited by 4
  7. 2023A Parameterized Theory of PAC Learningcited by 3
  8. 2023Structural Node Embeddings with Homomorphism Countscited by 3
  9. 2024Weisfeiler and Leman Go Loopy: A New Hierarchy for Graph Representational Learningcited by 2
  10. 2025Graph Homomorphism Distortion: A Metric to Distinguish Them All and in the Latent Space Bind Themcited by 1
  11. 2026Any-Dimensional Invariant Universalitycited by 1
  12. 2021Scaling up graph homomorphism for classification via samplingcited by 0
  13. 2021Learning on Random Balls is Sufficient for Estimating (Some) Graph Parameterscited by 0
  14. 2024Generalization of Graph Neural Networks through the Lens of Homomorphismcited by 0
  15. 2025Caterpillar GNN: Replacing Message Passing with Efficient Aggregationcited by 0