GraKeL: A Graph Kernel Library in Python
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.
Cite
Text
Siglidis et al. "GraKeL: A Graph Kernel Library in Python." Machine Learning Open Source Software, 2020.Markdown
[Siglidis et al. "GraKeL: A Graph Kernel Library in Python." Machine Learning Open Source Software, 2020.](https://mlanthology.org/mloss/2020/siglidis2020jmlr-grakel/)BibTeX
@article{siglidis2020jmlr-grakel,
title = {{GraKeL: A Graph Kernel Library in Python}},
author = {Siglidis, Giannis and Nikolentzos, Giannis and Limnios, Stratis and Giatsidis, Christos and Skianis, Konstantinos and Vazirgiannis, Michalis},
journal = {Machine Learning Open Source Software},
year = {2020},
pages = {1-5},
volume = {21},
url = {https://mlanthology.org/mloss/2020/siglidis2020jmlr-grakel/}
}