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Fast subtree kernels on graphs

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Shervashidze,  N
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Former Research Group Machine Learning and Computational Biology, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Borgwardt,  KM
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Former Research Group Machine Learning and Computational Biology, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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引用

Shervashidze, N., & Borgwardt, K. (2010). Fast subtree kernels on graphs. In Y., Bengio, D., Schuurmans, J., Lafferty, C., Williams, & A., Culotta (Eds.), Advances in Neural Information Processing Systems 22 (pp. 1660-1668). Red Hook, NY, USA: Curran.


引用: https://hdl.handle.net/11858/00-001M-0000-0013-C0C6-B
要旨
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.