graphkernels: R and Python packages for graph comparison

Abstract

Measuring the similarity of graphs is a fundamental step in the analysis of graphstructured 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 stateof-the-art Weisfeiler-Lehman graph kernel. The core of all graph kernels is implemented in C þþ 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.