English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Conference Paper

Shortest-path kernels on graphs

MPS-Authors
There are no MPG-Authors available
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
Citation

Borgwardt, K., & Kriegel, H.-P. (2005). Shortest-path kernels on graphs. In Fifth IEEE International Conference on Data Mining (ICDM'05) (pp. 74-81). Los Alamitos, CA, USA: IEEE Computer Society.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-D39F-C
Abstract
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.