Jiaqi Han, Wenbing Huang, Yu Rong, Tingyang Xu, Fuchun Sun, Junzhou Huang
In IEEE Transactions on Neural Networks and Learning Systems, 2023.
In Structure-Aware DropEdge, we enhance graph edge dropping technique with two structure-aware samplers, the layer-dependent sampler and feature-dependent sampler, to further relieve the over-smoothing issue in deep graph networks.
Please check out the Python environment depicted in requirements.txt
.
The semi-supervised setting strictly follows GCN, and the full-supervised setting follows DropEdge. The co-author and co-purchase datasets can be downloaded from https://github.com/shchur/gnn-benchmark.
The code has been tested in the above-mentioned environment with Python=3.6.2
. We recommend using conda.
conda create -n xxx python=3.6.2
conda activate xxx
pip install -r requirements.txt
To reproduce our results, just run the scripts in the scripts
folder. For example,
sh scripts/semi/citeseer_appnp.sh
If you find our work helpful, please cite as:
@ARTICLE{10195874,
author={Han, Jiaqi and Huang, Wenbing and Rong, Yu and Xu, Tingyang and Sun, Fuchun and Huang, Junzhou},
journal={IEEE Transactions on Neural Networks and Learning Systems},
title={Structure-Aware DropEdge Toward Deep Graph Convolutional Networks},
year={2023},
volume={},
number={},
pages={1-13},
doi={10.1109/TNNLS.2023.3288484}}
If you have any questions, feel free to reach us at:
Jiaqi Han: [email protected]