Transcription of Weisfeiler-Lehman Graph Kernels
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Weisfeiler-Lehman Graph Kernels
Journal of Machine Learning Research 12 (2011) 2539-2561 Submitted 5/10; Revised 6/11; Published 9/11 Weisfeiler-Lehman Graph KernelsNino Learning & Computational Biology Research GroupMax Planck Institutes T ubingenSpemannstr. 3872076 T ubingen, GermanyPascal Planck Institute for InformaticsCampus E1 466123 Saarbr ucken, GermanyErik Jan van of InformaticsUniversity of BergenPostboks 7803N-5020 Bergen, NorwayKurt Planck Institute for InformaticsCampus E1 466123 Saarbr ucken, GermanyKarsten M. Learning & Computational Biology Research GroupMax Planck Institutes T ubingenSpemannstr. 3872076 T ubingen, GermanyEditor:Francis BachAbstractIn this article, we propose a family of efficient Kernels for large graphs with discrete node la-bels.
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 experimentalevaluation, our kernels outperform
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