# GraKeL: A Graph Kernel Library in Python

arXiv: https://arxiv.org/abs/1806.02193 · HTML: https://mlsanity.com/paper/1806.02193/

## Key facts

- Cited by 54 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jun 2018.

## Built on by (most cited first, 30 of 54)

- [The Curious Decline of Linguistic Diversity: Training Language Models on Synthetic Text](https://mlsanity.com/paper/2311.09807/) (2023)
- [KerGNNs: Interpretable Graph Neural Networks with Graph Kernels](https://mlsanity.com/paper/2201.00491/) (2022)
- [On Evaluation Metrics for Graph Generative Models](https://mlsanity.com/paper/2201.09871/) (2022)
- [GAUCHE: A Library for Gaussian Processes in Chemistry](https://mlsanity.com/paper/2212.04450/) (2022)
- [Benchmarking Linguistic Diversity of Large Language Models](https://mlsanity.com/paper/2412.10271/) (2024)
- [Online Graph Dictionary Learning](https://mlsanity.com/paper/2102.06555/) (2021)
- [Inductive Graph Unlearning](https://mlsanity.com/paper/2304.03093/) (2023)
- [Do Large Language Models Have an English Accent? Evaluating and Improving the Naturalness of Multilingual LLMs](https://mlsanity.com/paper/2410.15956/) (2024)
- [Deep Orthogonal Hypersphere Compression for Anomaly Detection](https://mlsanity.com/paper/2302.06430/) (2023)
- [A Generalized Weisfeiler-Lehman Graph Kernel](https://mlsanity.com/paper/2101.08104/) (2021)
- [Graph Filtration Kernels](https://mlsanity.com/paper/2110.11862/) (2021)
- [Gaussian process regression with Sliced Wasserstein Weisfeiler-Lehman graph kernels](https://mlsanity.com/paper/2402.03838/) (2024)
- [Graph similarity learning for change-point detection in dynamic networks](https://mlsanity.com/paper/2203.15470/) (2022)
- [Metric Based Few-Shot Graph Classification](https://mlsanity.com/paper/2206.03695/) (2022)
- [Subsampling Graphs with GNN Performance Guarantees](https://mlsanity.com/paper/2502.16703/) (2025)
- [Orthogonal Gromov-Wasserstein Discrepancy with Efficient Lower Bound](https://mlsanity.com/paper/2205.05838/) (2022)
- [TREE-G: Decision Trees Contesting Graph Neural Networks](https://mlsanity.com/paper/2207.02760/) (2022)
- [Closing the Loop: Graph Networks to Unify Semantic Objects and Visual Features for Multi-object Scenes](https://mlsanity.com/paper/2209.11894/) (2022)
- [Deep Graph-Level Clustering Using Pseudo-Label-Guided Mutual Information Maximization Network](https://mlsanity.com/paper/2302.02369/) (2023)
- [Leveraging Discourse Structure for Extractive Meeting Summarization](https://mlsanity.com/paper/2405.11055/) (2024)
- [KEA Explain: Explanations of Hallucinations using Graph Kernel Analysis](https://mlsanity.com/paper/2507.03847/) (2025)
- [GENIUS: A Novel Solution for Subteam Replacement with Clustering-based Graph Neural Network](https://mlsanity.com/paper/2211.04100/) (2022)
- [Pattern Mining for Anomaly Detection in Graphs: Application to Fraud in Public Procurement](https://mlsanity.com/paper/2306.10857/) (2023)
- [Deep Sketched Output Kernel Regression for Structured Prediction](https://mlsanity.com/paper/2406.09253/) (2024)
- [Evaluating machine learning models for predicting pesticide toxicity to honey bees](https://mlsanity.com/paper/2503.24305/) (2025)
- [Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection](https://mlsanity.com/paper/2505.21285/) (2025)
- [Global optimization of graph acquisition functions for neural architecture search](https://mlsanity.com/paper/2505.23640/) (2025)
- [Bermuda Triangles: GNNs Fail to Detect Simple Topological Structures](https://mlsanity.com/paper/2105.00134/) (2021)
- [TrialGraph: Machine Intelligence Enabled Insight from Graph Modelling of Clinical Trials](https://mlsanity.com/paper/2112.08211/) (2021)
- [Multi-scale Wasserstein Shortest-path Graph Kernels for Graph Classification](https://mlsanity.com/paper/2206.00979/) (2022)

## 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 .