PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Fast subtree kernels on graphs
Nino Shervashidze and Karsten Borgwardt
In: Advances in Neural Information Processing Systems 22 (NIPS 2009), 7-10 Dec 2009, Vancouver, Canada.

Abstract

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(n^2 4^d h). 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.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:6394
Deposited By:Nino Shervashidze
Deposited On:08 March 2010