SUMMARY: Measuring 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 IMPLEMENTATION: The R and Python packages including source code 
are available at https://CRAN.R-project.org/package=graphkernels and 
https://pypi.python.org/pypi/graphkernels.
CONTACT: mahito@nii.ac.jp or elisabetta.ghisu@bsse.ethz.ch.
SUPPLEMENTARY INFORMATION: Supplementary data are available online at 
Bioinformatics.