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graphkernels: R and Python packages for graph comparison.

Mahito Sugiyama1,2, M Elisabetta Ghisu3,4, Felipe Llinares-López3,4

  • 1National Institute of Informatics, Chiyoda-ku, Tokyo 101-8430, Japan.

Bioinformatics (Oxford, England)
|October 14, 2017
PubMed
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We introduce graphkernels, the first R and Python libraries for graph similarity analysis. These libraries offer efficient graph kernel implementations for tasks like classification and clustering.

Area of Science:

  • Computational Biology
  • Bioinformatics
  • Machine Learning

Background:

  • Graph-structured data analysis is crucial in computational biology.
  • Graph kernels offer an efficient method for comparing graphs.
  • Existing tools lack comprehensive graph kernel implementations.

Purpose of the Study:

  • To introduce graphkernels, the first R and Python libraries for graph similarity analysis.
  • To provide efficient implementations of various graph kernels.
  • To facilitate graph-based machine learning tasks.

Main Methods:

  • Implementation of baseline, classic, and state-of-the-art graph kernels.
  • Core kernel computations optimized in C++ for efficiency.
  • Integration into user-friendly R and Python packages.

Related Experiment Videos

Main Results:

  • The graphkernels library includes label histogram, random walk, and Weisfeiler-Lehman kernels.
  • Kernel matrices generated by the package enable downstream analysis.
  • Demonstrated utility in classification, regression, and clustering tasks.

Conclusions:

  • graphkernels provides a versatile and efficient toolkit for graph comparison.
  • The libraries support a wide range of graph kernel methods.
  • Facilitates advanced analysis of graph-structured data in biology and beyond.