mlsanityData to 7 Oct 2026

GraKeL: A Graph Kernel Library in Python

arXiv 1806.02193 ↗First posted 6 Jun 2018stat.ML cs.LGcited by 54

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Abstract (arXiv)

The problem of accurately measuring the similarity between graphs is at the core of many applications in a variety of disciplines. Graph kernels have recently emerged as a promising approach to this problem. There are now many kernels, each focusing on different structural aspects of graphs. Here, we present GraKeL, a library that unifies several graph kernels into a common framework. The library is written in Python and adheres to the scikit-learn interface. It is simple to use and can be naturally combined with scikit-learn's modules to build a complete machine learning pipeline for tasks such as graph classification and clustering. The code is BSD licensed and is available at: https://github.com/ysig/GraKeL .

Key facts. Cited by 54 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Jun 2018.

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Citing papers per year: 2021: 7; 2022: 14; 2023: 7; 2024: 11; 2025: 10; 2026: 5.

54 papers.

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  1. 2023The Curious Decline of Linguistic Diversity: Training Language Models on Synthetic Textcited by 97
  2. 2022KerGNNs: Interpretable Graph Neural Networks with Graph Kernelscited by 50
  3. 2022On Evaluation Metrics for Graph Generative Modelscited by 44
  4. 2022GAUCHE: A Library for Gaussian Processes in Chemistrycited by 40
  5. 2021Online Graph Dictionary Learningcited by 37
  6. 2024Benchmarking Linguistic Diversity of Large Language Modelscited by 37
  7. 2023Inductive Graph Unlearningcited by 30
  8. 2024Do Large Language Models Have an English Accent? Evaluating and Improving the Naturalness of Multilingual LLMscited by 19
  9. 2023Deep Orthogonal Hypersphere Compression for Anomaly Detectioncited by 12
  10. 2021A Generalized Weisfeiler-Lehman Graph Kernelcited by 9
  11. 2021Graph Filtration Kernelscited by 8
  12. 2024Gaussian process regression with Sliced Wasserstein Weisfeiler-Lehman graph kernelscited by 7
38 more
  1. 2022Graph similarity learning for change-point detection in dynamic networkscited by 4
  2. 2022Metric Based Few-Shot Graph Classificationcited by 4
  3. 2025Subsampling Graphs with GNN Performance Guaranteescited by 4
  4. 2022Orthogonal Gromov-Wasserstein Discrepancy with Efficient Lower Boundcited by 3
  5. 2022TREE-G: Decision Trees Contesting Graph Neural Networkscited by 3
  6. 2022Closing the Loop: Graph Networks to Unify Semantic Objects and Visual Features for Multi-object Scenescited by 3
  7. 2023Deep Graph-Level Clustering Using Pseudo-Label-Guided Mutual Information Maximization Networkcited by 3
  8. 2024Leveraging Discourse Structure for Extractive Meeting Summarizationcited by 3
  9. 2025KEA Explain: Explanations of Hallucinations using Graph Kernel Analysiscited by 3
  10. 2022GENIUS: A Novel Solution for Subteam Replacement with Clustering-based Graph Neural Networkcited by 2
  11. 2023Pattern Mining for Anomaly Detection in Graphs: Application to Fraud in Public Procurementcited by 2
  12. 2024Deep Sketched Output Kernel Regression for Structured Predictioncited by 2
  13. 2025Evaluating machine learning models for predicting pesticide toxicity to honey beescited by 2
  14. 2025Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detectioncited by 2
  15. 2025Global optimization of graph acquisition functions for neural architecture searchcited by 2
  16. 2021Bermuda Triangles: GNNs Fail to Detect Simple Topological Structurescited by 1
  17. 2021TrialGraph: Machine Intelligence Enabled Insight from Graph Modelling of Clinical Trialscited by 1
  18. 2022Multi-scale Wasserstein Shortest-path Graph Kernels for Graph Classificationcited by 1
  19. 2022Efficient multi-relational network representation using primescited by 1
  20. 2024Learning Attributed Graphlets: Predictive Graph Mining by Graphlets with Trainable Attributecited by 1
  21. 2024Exploring Consistency in Graph Representations:from Graph Kernels to Graph Neural Networkscited by 1
  22. 2025Metric Graph Kernels via the Tropical Torelli Mapcited by 1
  23. 2021GLAMOUR: Graph Learning over Macromolecule Representationscited by 0
  24. 2021Matching Models for Graph Retrievalcited by 0
  25. 2022Topological Classification in a Wasserstein Distance Based Vector Spacecited by 0
  26. 2022Similarity-aware Positive Instance Sampling for Graph Contrastive Pre-trainingcited by 0
  27. 2022Modeling the Social Influence of COVID-19 via Personalized Propagation with Deep Learningcited by 0
  28. 2023Weisfeiler and Leman Go Measurement Modeling: Probing the Validity of the WL Testcited by 0
  29. 2023Attacking the Loop: Adversarial Attacks on Graph-based Loop Closure Detectioncited by 0
  30. 2024Message Detouring: A Simple Yet Effective Cycle Representation for Expressive Graph Learningcited by 0
  31. 2024Multiple-Source Localization from a Single-Snapshot Observation Using Graph Bayesian Optimizationcited by 0
  32. 2024Imbalanced Graph Classification with Multi-scale Oversampling Graph Neural Networkscited by 0
  33. 2024Deep Hierarchical Graph Alignment Kernelscited by 0
  34. 2025Graph Learning via Logic-Based Weisfeiler-Leman Variants and Tabularizationcited by 0
  35. 2025Representing Classical Compositions through Implication-Realization Temporal-Gestalt Graphscited by 0
  36. 2025GraphToxin: Reconstructing Full Unlearned Graphs from Graph Unlearningcited by 0
  37. 2025Explainable Graph Representation Learning via Graph Pattern Analysiscited by 0
  38. 2026Diversity Curves for Graph Representation Learningcited by 0

50 most cited of 54 All 54 (JSON)