National Institute of Informatics, Chiyoda-ku, Tokyo 101-8430, Japan.
JST PRESTO, Kawaguchi, Saitama 332-0012, Japan.
Bioinformatics. 2018 Feb 1;34(3):530-532. doi: 10.1093/bioinformatics/btx602.
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.
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.
mahito@nii.ac.jp or elisabetta.ghisu@bsse.ethz.ch.
Supplementary data are available online at Bioinformatics.
衡量图的相似性是分析图结构数据的基本步骤，而图结构数据在计算生物学中无处不在。图核已被提出作为一种强大而有效的图比较问题的解决方案。在这里，我们提供了 graphkernels，这是第一个包含基于标签直方图的核等基线核、基于随机游走的核等经典核以及最新的 Weisfeiler-Lehman 图核的 R 和 Python 图核库。所有图核的核心都是用 C++ 实现的，以提高效率。使用该软件包计算的核矩阵，我们可以轻松地在图结构样本上执行分类、回归和聚类等任务。
包括源代码的 R 和 Python 包可在 https://CRAN.R-project.org/package=graphkernels 和 https://pypi.python.org/pypi/graphkernels 获得。
mahito@nii.ac.jp 或 elisabetta.ghisu@bsse.ethz.ch。
补充数据可在 Bioinformatics 上在线获得。
