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Journal Article

graphkernels: R and Python packages for graph comparison

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Sugiyama, M., Ghisu, M. E., Llinares-López, F., & Borgwardt, K. (2017). graphkernels: R and Python packages for graph comparison. Bioinformatics, 34(3), 530-532. doi:10.1093/bioinformatics/btx602.

Cite as: https://hdl.handle.net/21.11116/0000-000C-F283-E
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. Supplementary information Supplementary data are available online at Bioinformatics.