Associate Editor1 National Institute of Informatics, Chiyoda-ku, Tokyo, Japan2 JST PRESTO, Kawaguchi, Saitama, Japan3 D-BSSE, ETH Zürich, Switzerland4 Swiss Institute of Bioinformatics, Basel, Switzerland
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SummaryMeasuring the similarity of graphs is a fundamental step in the analysis of graph-structured data, which is omnipresent in computational biology. Graph kernels have been proposed as a powerful and efficient approach to this problem of graph comparison. Here we provide graphkernels, the first R and Python graph kernel libraries including baseline kernels such as label histogram based kernels, classic graph kernels such as random walk based kernels, and the state-of-the-art Weisfeiler-Lehman graph kernel. The core of all graph kernels is implemented in C ++ for efficiency. Using the kernel matrices computed by the package, we can easily perform tasks such as classification, regression and clustering on graph-structured samples.Availability and implementationThe R and Python packages including source code are available at https://CRAN.R-project.org/package=graphkernels and https://pypi.python.org/pypi/graphkernels.