RetGK: Graph Kernels Based on Return Probabilities of Random Walks
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.
Cite
Text
Zhang et al. "RetGK: Graph Kernels Based on Return Probabilities of Random Walks." Neural Information Processing Systems, 2018.Markdown
[Zhang et al. "RetGK: Graph Kernels Based on Return Probabilities of Random Walks." Neural Information Processing Systems, 2018.](https://mlanthology.org/neurips/2018/zhang2018neurips-retgk/)BibTeX
@inproceedings{zhang2018neurips-retgk,
title = {{RetGK: Graph Kernels Based on Return Probabilities of Random Walks}},
author = {Zhang, Zhen and Wang, Mianzhi and Xiang, Yijian and Huang, Yan and Nehorai, Arye},
booktitle = {Neural Information Processing Systems},
year = {2018},
pages = {3964-3974},
url = {https://mlanthology.org/neurips/2018/zhang2018neurips-retgk/}
}