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  Weisfeiler-Lehman Graph Kernels

Shervashidze, N., Schweitzer, P., van Leeuwen, E., Mehlhorn, K., & Borgwardt, M. (2011). Weisfeiler-Lehman Graph Kernels. The Journal of Machine Learning Research, 12, 2539-2561.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-BA34-D Version Permalink: http://hdl.handle.net/21.11116/0000-0006-C598-0
Genre: Journal Article

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 Creators:
Shervashidze, N1, Author              
Schweitzer, P, Author
van Leeuwen , EJ, Author
Mehlhorn, K, Author
Borgwardt, M1, Author              
Affiliations:
1Former Research Group Machine Learning and Computational Biology, Max Planck Institute for Intelligent Systems, Max Planck Society, DE, ou_1497664              

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 Abstract: In this article, we propose a family of efficient kernels for large graphs with discrete node labels. Key to our method is a rapid feature extraction scheme based on the Weisfeiler-Lehman test of isomorphism on graphs. It maps the original graph to a sequence of graphs, whose node attributes capture topological and label information. A family of kernels can be defined based on this Weisfeiler-Lehman sequence of graphs, including a highly efficient kernel comparing subtree-like patterns. Its runtime scales only linearly in the number of edges of the graphs and the length of the Weisfeiler-Lehman graph sequence. In our experimental evaluation, our kernels outperform state-of-the-art graph kernels on several graph classification benchmark data sets in terms of accuracy and runtime. Our kernels open the door to large-scale applications of graph kernels in various disciplines such as computational biology and social network analysis.

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 Dates: 2011-09
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Method: -
 Identifiers: BibTex Citekey: ShervashidzeSvMB2011
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Title: The Journal of Machine Learning Research
Source Genre: Journal
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Publ. Info: Cambridge, MA : MIT Press
Pages: - Volume / Issue: 12 Sequence Number: - Start / End Page: 2539 - 2561 Identifier: ISSN: 1532-4435
CoNE: https://pure.mpg.de/cone/journals/resource/111002212682020_1