NeurIPS 2018poster124 citations
RetGK: Graph Kernels based on Return Probabilities of Random Walks
Zhen Zhang, Mianzhi Wang, Yijian Xiang, Yan Huang, Arye Nehorai
Abstract
Graph-structured data arise in wide applications, such as computer vision, bioinformatics, and social networks. Quantifying similarities among graphs is a fundamental problem. In this paper, we develop a framework for computing graph kernels, based on return probabilities of random walks. The advantages of our proposed kernels are that they can effectively exploit various node attributes, while being scalable to large datasets. We conduct extensive graph classification experiments to evaluate our graph kernels. The experimental results show that our graph kernels significantly outperform other state-of-the-art approaches in both accuracy and computational efficiency.
BibTeX
@inproceedings{NEURIPS2018_7f16109f,
author = {Zhang, Zhen and Wang, Mianzhi and Xiang, Yijian and Huang, Yan and Nehorai, Arye},
booktitle = {Advances in Neural Information Processing Systems},
editor = {S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {RetGK: Graph Kernels based on Return Probabilities of Random Walks},
url = {https://proceedings.neurips.cc/paper_files/paper/2018/file/7f16109f1619fd7a733daf5a84c708c1-Paper.pdf},
volume = {31},
year = {2018}
}