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Recent Advances in Kernel-Based Graph Classification

  • Nils M. KriegeEmail author
  • Christopher Morris
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10536)

Abstract

We review our recent progress in the development of graph kernels. We discuss the hash graph kernel framework, which makes the computation of kernels for graphs with vertices and edges annotated with real-valued information feasible for large data sets. Moreover, we summarize our general investigation of the benefits of explicit graph feature maps in comparison to using the kernel trick. Our experimental studies on real-world data sets suggest that explicit feature maps often provide sufficient classification accuracy while being computed more efficiently. Finally, we describe how to construct valid kernels from optimal assignments to obtain new expressive graph kernels. These make use of the kernel trick to establish one-to-one correspondences. We conclude by a discussion of our results and their implication for the future development of graph kernels.

Notes

Acknowledgement

We would like to thank the co-authors of our publications [10, 11, 12, 14]. This research was supported by the German Science Foundation (DFG) within the Collaborative Research Center SFB 876 “Providing Information by Resource-Constrained Data Analysis”, project A6.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Computer ScienceTU Dortmund UniversityDortmundGermany

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