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Graph Kernels: State-of-the-Art and Future Challenges

arXiv 2011.03854 ↗First posted 7 Nov 2020cs.LG stat.MLcited by 63

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Abstract (arXiv)

Graph-structured data are an integral part of many application domains, including chemoinformatics, computational biology, neuroimaging, and social network analysis. Over the last two decades, numerous graph kernels, i.e. kernel functions between graphs, have been proposed to solve the problem of assessing the similarity between graphs, thereby making it possible to perform predictions in both classification and regression settings. This manuscript provides a review of existing graph kernels, their applications, software plus data resources, and an empirical comparison of state-of-the-art graph kernels.

Key facts. Cited by 63 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Nov 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 11; 2022: 15; 2023: 9; 2024: 11; 2025: 12; 2026: 5.

63 papers.

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  1. 2021GraphiT: Encoding Graph Structure in Transformerscited by 146
  2. 2021Weisfeiler and Leman go Machine Learning: The Story so farcited by 121
  3. 2021Topological Graph Neural Networkscited by 86
  4. 2022Ordered Subgraph Aggregation Networkscited by 57
  5. 2021Combining Latent Space and Structured Kernels for Bayesian Optimization over Combinatorial Spacescited by 45
  6. 2021Modular Design Patterns for Hybrid Learning and Reasoning Systems: a taxonomy, patterns and use casescited by 39
  7. 2022Tree Mover's Distance: Bridging Graph Metrics and Stability of Graph Neural Networkscited by 39
  8. 2021Evaluation Metrics for Graph Generative Models: Problems, Pitfalls, and Practical Solutionscited by 38
  9. 2022SpeqNets: Sparsity-aware Permutation-equivariant Graph Networkscited by 37
  10. 2023Path Neural Networks: Expressive and Accurate Graph Neural Networkscited by 32
  11. 2023WL meet VCcited by 30
  12. 2022HAQJSK: Hierarchical-Aligned Quantum Jensen-Shannon Kernels for Graph Classificationcited by 18
38 more
  1. 2023Curvature Filtrations for Graph Generative Model Evaluationcited by 18
  2. 2022SGAT: Simplicial Graph Attention Networkcited by 16
  3. 2025No Metric to Rule Them All: Toward Principled Evaluations of Graph-Learning Datasetscited by 16
  4. 2024Weisfeiler-Leman at the margin: When more expressivity matterscited by 13
  5. 2022Capturing Graphs with Hypo-Elliptic Diffusionscited by 12
  6. 2024The GeometricKernels Package: Heat and Mat'ern Kernels for Geometric Learning on Manifolds, Meshes, and Graphscited by 10
  7. 2021Graph Filtration Kernelscited by 8
  8. 2022Weisfeiler and Leman Go Walking: Random Walk Kernels Revisitedcited by 8
  9. 2024Gaussian process regression with Sliced Wasserstein Weisfeiler-Lehman graph kernelscited by 7
  10. 2024Diss-l-ECT: Dissecting Graph Data with Local Euler Characteristic Transformscited by 6
  11. 2021Fast Topological Clustering with Wasserstein Distancecited by 5
  12. 2022Multiscale Graph Comparison via the Embedded Laplacian Discrepancycited by 5
  13. 2022Maximum Mean Discrepancy on Exponential Windows for Online Change Detectioncited by 5
  14. 2022Gradual Weisfeiler-Leman: Slow and Steady Wins the Racecited by 5
  15. 2023Nystr"om M-Hilbert-Schmidt Independence Criterioncited by 5
  16. 2023Sketch In, Sketch Out: Accelerating both Learning and Inference for Structured Prediction with Kernelscited by 5
  17. 2023Graph Classification Gaussian Processes via Spectral Featurescited by 4
  18. 2021EGC2: Enhanced Graph Classification with Easy Graph Compressioncited by 3
  19. 2022TREE-G: Decision Trees Contesting Graph Neural Networkscited by 3
  20. 2023Contrastive Learning for Non-Local Graphs with Multi-Resolution Structural Viewscited by 3
  21. 2024Graph Classification Gaussian Processes via Hodgelet Spectral Featurescited by 3
  22. 2021Keep it Tighter -- A Story on Analytical Mean Embeddingscited by 2
  23. 2024Deep Sketched Output Kernel Regression for Structured Predictioncited by 2
  24. 2024Graph-Based Representation Learning of Neuronal Dynamics and Behaviorcited by 2
  25. 2025Evaluating machine learning models for predicting pesticide toxicity to honey beescited by 2
  26. 2025Global optimization of graph acquisition functions for neural architecture searchcited by 2
  27. 2025Understanding Generalization in Node and Link Predictioncited by 2
  28. 2026Have Graph -- Will Lift? The Case for Higher-Order Benchmarkscited by 2
  29. 2026Minimax Lower Bounds of Kernel Discrepancy Estimation: MMD, HSIC, KSDcited by 2
  30. 2021TrialGraph: Machine Intelligence Enabled Insight from Graph Modelling of Clinical Trialscited by 1
  31. 2022Literature Review: Graph Kernels in Chemoinformaticscited by 1
  32. 2022Efficient multi-relational network representation using primescited by 1
  33. 2023Fisher Information Embedding for Node and Graph Learningcited by 1
  34. 2024Graph Vertex Embeddings: Distance, Regularization and Community Detectioncited by 1
  35. 2024TopER: Topological Embeddings in Graph Representation Learningcited by 1
  36. 2025Scalable Sobolev IPM for Probability Measures on a Graphcited by 1
  37. 2025Graph and Simplicial Complex Prediction Gaussian Process via the Hodgelet Representationscited by 1
  38. 2025Metric Graph Kernels via the Tropical Torelli Mapcited by 1

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