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Graphkernels: R and Python Packages for Graph Comparison

Overview
Journal Bioinformatics
Specialty Biology
Date 2017 Oct 14
PMID 29028902
Citations 7
Authors
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Abstract

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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