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Journal Articles Journal of Machine Learning Research Year : 2020

GraKeL: A Graph Kernel Library in Python


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: GraKeL.
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Dates and versions

hal-02612740 , version 1 (19-05-2020)



Giannis Siglidis, Giannis Nikolentzos, Stratis Limnios, Christos Giatsidis, Konstantinos Skianis, et al.. GraKeL: A Graph Kernel Library in Python. Journal of Machine Learning Research, 2020. ⟨hal-02612740⟩
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