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Conference Paper

Fast subtree kernels on graphs


Shervashidze,  N
Max Planck Institute for Biological Cybernetics, Max Planck Society;


Borgwardt,  KM
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Shervashidze, N., & Borgwardt, K. (2010). Fast subtree kernels on graphs. Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009, 1660-1668.

Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-C0C6-B
In this article, we propose fast subtree kernels on graphs. On graphs with n nodes and m edges and maximum degree d, these kernels comparing subtrees of height h can be computed in O(mh), whereas the classic subtree kernel by Ramon Gärtner scales as O(n24dh). Key to this efficiency is the observation that the Weisfeiler-Lehman test of isomorphism from graph theory elegantly computes a subtree kernel as a byproduct. Our fast subtree kernels can deal with labeled graphs, scale up easily to large graphs and outperform state-of-the-art graph kernels on several classification benchmark datasets in terms of accuracy and runtime.