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.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5860361 | PMC |
| http://dx.doi.org/10.1093/bioinformatics/btx602 | DOI Listing |
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