A transitive aligned Weisfeiler-Lehman subtree kernel

Abstract

In this paper, we develop a new transitive aligned Weisfeiler-Lehman subtree kernel. This kernel not only overcomes the shortcoming of ignoring correspondence information between isomorphic substructures that arises in existing R-convolution kernels, but also guarantees the transitivity between the correspondence information that is not available for existing matching kernels. Our kernel outperforms state-of-the-art graph kernels in terms of classification accuracy on standard graph datasets.

Publication DOI: https://doi.org/10.1109/ICPR.2016.7899666
Divisions: College of Engineering & Physical Sciences
College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
Additional Information: -© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Event Title: 23rd International Conference on Pattern Recognition
Event Type: Other
Event Dates: 2016-12-04 - 2016-12-08
Uncontrolled Keywords: Computer Vision and Pattern Recognition
ISBN: 978-1-5090-4847-2
Full Text Link:
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Conference contribution
Published Date: 2017-04-13
Accepted Date: 2016-07-13
Authors: Bai, Lu
Rossi, Luca (ORCID Profile 0000-0002-6116-9761)
Cui, Lixin
Hancock, Edwin R.

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Version: Accepted Version


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