GraKeL: A Graph Kernel Library in Python

TL;DR

GraKeL is a Python library that unifies multiple graph kernels within a scikit-learn compatible framework, facilitating graph similarity measurement and machine learning tasks like classification and clustering.

Contribution

It introduces a unified, easy-to-use Python library for various graph kernels, enabling seamless integration with machine learning workflows.

Findings

Supports multiple graph kernels in a single framework

Compatible with scikit-learn for pipeline integration

Simplifies graph similarity analysis and learning tasks

Abstract

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 .