2018

GraKeL: A Graph Kernel Library in Python

Siglidis, Giannis, Nikolentzos, Giannis, Limnios, Stratis et al.

Understand

The problem of accurately measuring the similarity between graphs is at the core of many applications in a variety of disciplines.

  • Graph kernels have recently emerged as a promising approach to this problem.
  • There are now many kernels, each focusing on different structural aspects of graphs.
  • Here, we present GraKeL, a library that unifies several graph kernels into a common framework.

Built on

  • Engineering an Efficient Canonical Labeling Toolfor Large and Sparse Graphs

    Tommi Junttila and Petteri Kaski · 2007

    Earlier work this paper cites.

  • Cython: The Best of Both Worlds

    Stefan Behnel, Robert Bradshaw, Craig Citro, Lisandro Dalcin, Dag Sverre Seljebotn, and Kurt Smith · 2011

    Earlier work this paper cites.

  • Scikit-learn: Machine Learning in Python

    Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake Vanderplas, Alexandre Passos, and David Cournapeau · 2011

    Earlier work this paper cites.

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Then

  • Benchmark Data Sets for Graph Kernels, 2016

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    Later among the works it cites.

  • graphkernels: R and Python packages for graph comparison

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    Later among the works it cites.

  • SciPy 1.0: fundamental algorithms for scientific computing in Python

    Pauli Virtanen, Ralf Gommers, Travis E Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, et al · 2020

    Closest in time.

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