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  Fast Computation of Graph Kernels

Vishwanathan, S., Borgwardt, K., & Schraudolph, N. (2007). Fast Computation of Graph Kernels. In Advances in Neural Information Processing Systems 19 (pp. 1449-1456). Cambridge, MA, USA: MIT Press.

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 Creators:
Vishwanathan, SVN, Author
Borgwardt, KM1, Author           
Schraudolph, N, Author
Schölkopf, Editor
B., Editor
Platt, J., Editor
Hofmann, T., Editor
Affiliations:
1Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              

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 Abstract: Using extensions of linear algebra concepts to Reproducing Kernel Hilbert Spaces (RKHS), we define a unifying framework for random walk kernels on graphs. Reduction to a Sylvester equation allows us to compute many of these kernels in O(n3) worst-case time. This includes kernels whose previous worst-case time complexity was O(n6), such as the geometric kernels of G¨artner et al. [1] and the marginal graph kernels of Kashima et al. [2]. Our algebra in RKHS allow us to exploit sparsity in directed and undirected graphs more effectively than previous methods, yielding sub-cubic computational complexity when combined with conjugate gradient solvers or fixed-point iterations. Experiments on graphs from bioinformatics and other application domains show that our algorithms are often more than 1000 times faster than existing approaches.

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 Dates: 2007-09
 Publication Status: Issued
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: ISBN: 0-262-19568-2
URI: http://nips.cc/Conferences/2006/
BibTex Citekey: VishwanathanBS2007
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Title: Twentieth Annual Conference on Neural Information Processing Systems (NIPS 2006)
Place of Event: Vancouver, BC, Canada
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Title: Advances in Neural Information Processing Systems 19
Source Genre: Proceedings
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Publ. Info: Cambridge, MA, USA : MIT Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 1449 - 1456 Identifier: -