An Aligned Subtree Kernel for Weighted Graphs
Abstract
In this paper, we develop a new entropic matching kernel for weighted graphs by aligning depth-based representations. We demonstrate that this kernel can be seen as an \textbf{aligned} subtree kernel that incorporates explicit subtree correspondences, and thus addresses the drawback of neglecting the relative locations between substructures that arises in the R-convolution kernels. Experiments on standard datasets demonstrate that our kernel can easily outperform state-of-the-art graph kernels in terms of classification accuracy.
Cite
Text
Bai et al. "An Aligned Subtree Kernel for Weighted Graphs." International Conference on Machine Learning, 2015.Markdown
[Bai et al. "An Aligned Subtree Kernel for Weighted Graphs." International Conference on Machine Learning, 2015.](https://mlanthology.org/icml/2015/bai2015icml-aligned/)BibTeX
@inproceedings{bai2015icml-aligned,
title = {{An Aligned Subtree Kernel for Weighted Graphs}},
author = {Bai, Lu and Rossi, Luca and Zhang, Zhihong and Hancock, Edwin},
booktitle = {International Conference on Machine Learning},
year = {2015},
pages = {30-39},
volume = {37},
url = {https://mlanthology.org/icml/2015/bai2015icml-aligned/}
}