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.
|Title of host publication||2016 23rd International Conference on Pattern Recognition, ICPR|
|Number of pages||6|
|Publication status||Published - 13 Apr 2017|
|Event||23rd International Conference on Pattern Recognition: ICPR 2016 - Cancun, Mexico|
Duration: 4 Dec 2016 → 8 Dec 2016
|Conference||23rd International Conference on Pattern Recognition|
|Period||4/12/16 → 8/12/16|