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Shortest-path kernels on graphs


Conference Paper


Data mining algorithms are facing the challenge to deal with an increasing number of complex objects. For graph data, a whole toolbox of data mining algorithms becomes available by defining a kernel function on instances of graphs. Graph kernels based on walks, subtrees and cycles in graphs have been proposed so far. As a general problem, these kernels are either computationally expensive or limited in their expressiveness. We try to overcome this problem by defining expressive graph kernels which are based on paths. As the computation of all paths and longest paths in a graph is NP-hard, we propose graph kernels based on shortest paths. These kernels are computable in polynomial time, retain expressivity and are still positive definite. In experiments on classification of graph models of proteins, our shortest-path kernels show significantly higher classification accuracy than walk-based kernels.

Author(s): Borgwardt, KM. and Kriegel, H-P.
Pages: 74-81
Year: 2005
Month: November
Day: 0
Publisher: IEEE Computer Society

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

DOI: 10.1109/ICDM.2005.132
Event Name: Fifth International Conference on Data Mining (ICDM 2005)
Event Place: Houston, TX, USA

Address: Los Alamitos, CA, USA
Digital: 0
ISBN: 0-7695-2278-5

Links: Web


  title = {Shortest-path kernels on graphs},
  author = {Borgwardt, KM. and Kriegel, H-P.},
  pages = {74-81},
  publisher = {IEEE Computer Society},
  address = {Los Alamitos, CA, USA},
  month = nov,
  year = {2005},
  month_numeric = {11}