  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 .
