A survey on graph kernels
Review article, 2020

Graph kernels have become an established and widely-used technique for solving classification tasks on graphs. This survey gives a comprehensive overview of techniques for kernel-based graph classification developed in the past 15 years. We describe and categorize graph kernels based on properties inherent to their design, such as the nature of their extracted graph features, their method of computation and their applicability to problems in practice. In an extensive experimental evaluation, we study the classification accuracy of a large suite of graph kernels on established benchmarks as well as new datasets. We compare the performance of popular kernels with several baseline methods and study the effect of applying a Gaussian RBF kernel to the metric induced by a graph kernel. In doing so, we find that simple baselines become competitive after this transformation on some datasets. Moreover, we study the extent to which existing graph kernels agree in their predictions (and prediction errors) and obtain a data-driven categorization of kernels as result. Finally, based on our experimental results, we derive a practitioner’s guide to kernel-based graph classification.

Supervised graph classification

Machine learning

Graph kernels


Nils M. Kriege

Technische Universität Dortmund

Fredrik Johansson

Chalmers, Computer Science and Engineering (Chalmers), Data Science

Christopher Morris

Technische Universität Dortmund

Applied Network Science

23648228 (eISSN)

Vol. 5 1 6

Subject Categories

Computer Science

Discrete Mathematics

Computer Vision and Robotics (Autonomous Systems)



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