Training code for the paper [Rethinking Kernel Methods for Node Representation Learning on Graphs] (https://arxiv.org/pdf/1910.02548.pdf), NIPS 2019
We present a novel theoretical kernel-based framework for node classification. Our approach is motivated by graph kernel methodology but extended to learn the node representations capturing the structural information in a graph. We theoretically show that our formulation is as powerful as any positive semidefinite kernels. Our framework is flexible and complementary to other graph-based deep learning models, e.g., Graph Convolutional Networks (GCNs).
This package has the following requirements:
Python 3.6
Pytorch 0.4.1
numpy
scipy
networkx
python train.py
If you find this code useful in your research, please consider citing:
@inproceedings{tian2019rethinking,
title={Rethinking kernel methods for node representation learning on graphs},
author={Tian, Yu and Zhao, Long and Peng, Xi and Metaxas, Dimitris},
booktitle={Advances in Neural Information Processing Systems},
pages={11681--11692},
year={2019}
}