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GraKeL: A Graph Kernel Library in Python (2020)

In: Journal of Machine Learning Research

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Publication Date: 2020

Description: 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.

Print ISSN: 1532-4435

Electronic ISSN: 1533-7928

Topics: Computer Science

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