GraKeL: A Graph Kernel Library in Python
This provides a unified software solution for researchers and practitioners working with graph data, but it is incremental as it packages existing kernels rather than introducing new methods.
The authors tackled the problem of measuring graph similarity by developing GraKeL, a Python library that unifies multiple graph kernels into a common framework, resulting in a tool that integrates with scikit-learn for tasks like graph classification and clustering.
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 .