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About this paper
Giannis Siglidis, Giannis Nikolentzos, Stratis Limnios, Christos Giatsidis, Konstantinos Skianis, Michalis Vazirgiannis
GraKeL provides a shared scikit-learn-style interface for graph kernels, letting users compute training and test kernel matrices for compatible learners. The paper compares GraKeL with graphkernels on ENZYMES: GraKeL is faster for four of five displayed kernels, but slower for Weisfeiler–Lehman Subtree. This is a single-dataset implementation comparison, not evidence of universal speed superiority.
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