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Weisfeiler-Lehman Graph Kernels




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.

Author(s): Shervashidze, N. and Schweitzer, P. and van Leeuwen, EJ. and Mehlhorn, K. and Borgwardt, M.
Journal: Journal of Machine Learning Research
Volume: 12
Pages: 2539--2561
Year: 2011
Month: September
Day: 0

Department(s): Empirical Inference
Bibtex Type: Article (article)

Digital: 0

Links: PDF


  title = {Weisfeiler-Lehman Graph Kernels },
  author = {Shervashidze, N. and Schweitzer, P. and van Leeuwen, EJ. and Mehlhorn, K. and Borgwardt, M.},
  journal = {Journal of Machine Learning Research},
  volume = {12},
  pages = {2539--2561},
  month = sep,
  year = {2011},
  month_numeric = {9}