mlsanityData to 14 Oct 2026

Graph Kernels: A Survey

arXiv 1904.12218 ↗First posted 27 Apr 2019stat.ML cs.LGcited by 44

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Abstract (arXiv)

Graph kernels have attracted a lot of attention during the last decade, and have evolved into a rapidly developing branch of learning on structured data. During the past 20 years, the considerable research activity that occurred in the field resulted in the development of dozens of graph kernels, each focusing on specific structural properties of graphs. Graph kernels have proven successful in a wide range of domains, ranging from social networks to bioinformatics. The goal of this survey is to provide a unifying view of the literature on graph kernels. In particular, we present a comprehensive overview of a wide range of graph kernels. Furthermore, we perform an experimental evaluation of several of those kernels on publicly available datasets, and provide a comparative study. Finally, we discuss key applications of graph kernels, and outline some challenges that remain to be addressed.

Key facts. Cited by 44 papers in the map (data through 14 Oct 2026). First posted on arXiv 27 Apr 2019.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 2; 2022: 11; 2023: 4; 2024: 13; 2025: 11; 2026: 3.

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  1. 2021Topological Graph Neural Networkscited by 87
  2. 2022GAUCHE: A Library for Gaussian Processes in Chemistrycited by 41
  3. 2023Path Neural Networks: Expressive and Accurate Graph Neural Networkscited by 32
  4. 2025Nested Graph Pseudo-Label Refinement for Noisy Label Domain Adaptation Learningcited by 10
  5. 2022Graph Anomaly Detection with Unsupervised GNNscited by 8
  6. 2022Micro and Macro Level Graph Modeling for Graph Variational Auto-Encoderscited by 8
  7. 2022Isotropic Gaussian Processes on Finite Spaces of Graphscited by 7
  8. 2023Efficient Graph Field Integrators Meet Point Cloudscited by 7
  9. 2024Gaussian process regression with Sliced Wasserstein Weisfeiler-Lehman graph kernelscited by 7
  10. 2022Multiscale Graph Comparison via the Embedded Laplacian Discrepancycited by 5
  11. 2022Generating Synthetic Mobility Networks with Generative Adversarial Networkscited by 5
  12. 2024Descriptive Kernel Convolution Network with Improved Random Walk Kernelcited by 5
32 more
  1. 2022Wasserstein Graph Distance Based on L1-Approximated Tree Edit Distance between Weisfeiler-Lehman Subtreescited by 4
  2. 2023Graph Classification Gaussian Processes via Spectral Featurescited by 4
  3. 2023Structural Node Embeddings with Homomorphism Countscited by 4
  4. 2025Modeling Hierarchical Spaces: A Review and Unified Framework for Surrogate-Based Architecture Designcited by 4
  5. 2025HGCN(O): A Self-Tuning GCN HyperModel Toolkit for Outcome Prediction in Event-Sequence Datacited by 4
  6. 2022TREE-G: Decision Trees Contesting Graph Neural Networkscited by 3
  7. 2024Fast Tree-Field Integrators: From Low Displacement Rank to Topological Transformerscited by 3
  8. 2024Optimal Time Complexity Algorithms for Computing General Random Walk Graph Kernels on Sparse Graphscited by 3
  9. 2025Global optimization of graph acquisition functions for neural architecture searchcited by 2
  10. 2025Computationally-efficient Graph Modeling with Refined Graph Random Featurescited by 2
  11. 2024Explaining the Power of Topological Data Analysis in Graph Machine Learningcited by 1
  12. 2024Graph Edits for Counterfactual Explanations: A comparative studycited by 1
  13. 2024Learning signals defined on graphs with optimal transport and Gaussian process regressioncited by 1
  14. 2024Exploring Consistency in Graph Representations:from Graph Kernels to Graph Neural Networkscited by 1
  15. 2025Scalable Sobolev IPM for Probability Measures on a Graphcited by 1
  16. 2025Metric Graph Kernels via the Tropical Torelli Mapcited by 1
  17. 2025Fast, Accurate and Interpretable Graph Classification with Topological Kernelscited by 1
  18. 2021Generating the Graph Gestalt: Kernel-Regularized Graph Representation Learningcited by 0
  19. 2022GraphDCA -- a Framework for Node Distribution Comparison in Real and Synthetic Graphscited by 0
  20. 2022DAMNETS: A Deep Autoregressive Model for Generating Markovian Network Time Seriescited by 0
  21. 2022Graph-level Neural Networks: Current Progress and Future Directionscited by 0
  22. 2024Multiple-Source Localization from a Single-Snapshot Observation Using Graph Bayesian Optimizationcited by 0
  23. 2024Graph Neural Network based Handwritten Trajectories Recognitioncited by 0
  24. 2024A Survey of Latent Factor Models in Recommender Systemscited by 0
  25. 2024A Deep Learning Framework for Evaluating Dynamic Network Generative Models and Anomaly Detectioncited by 0
  26. 2024GSpect: Spectral Filtering for Cross-Scale Graph Classificationcited by 0
  27. 2025An Efficient Orlicz-Sobolev Approach for Transporting Unbalanced Measures on a Graphcited by 0
  28. 2025Measuring Similarity in Causal Graphs: A Framework for Semantic and Structural Analysiscited by 0
  29. 2025Simplifying Graph Kernels for Efficientcited by 0
  30. 2026Diversity Curves for Graph Representation Learningcited by 0
  31. 2026A General Kernel Framework for Non-CND Distance Measures Using |D|-Dimensional Sparse Landmark Embeddingscited by 0
  32. 2026From Imbalance to Evidence: Deep Nonparametric Testing for Complex Data under Severe Sample Imbalancecited by 0