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.
Similar
Weisfeiler-Lehman Graph Kernels
Nino Shervashidze, Pascal Schweitzer, Erik Jan van Leeuwen, Kurt Mehlhorn, and Karsten M Borgwardt · 2011
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The NumPy array: a structure for efficient numerical computation
Stéfan van der Walt, S Chris Colbert, and Gael Varoquaux · 2011
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CVXOPT: A Python package for convex optimization
Martin S Andersen, Joachim Dahl, and Lieven Vandenberghe · 2013
Cited alongside, same era.
Then
Benchmark Data Sets for Graph Kernels, 2016
Kristian Kersting, Nils M. Kriege, Christopher Morris, Petra Mutzel, and Marion Neumann · 2016
Later among the works it cites.
graphkernels: R and Python packages for graph comparison
Mahito Sugiyama, M Elisabetta Ghisu, Felipe Llinares-López, and Karsten Borgwardt · 2017
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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