Pytorch implementation of eigenpooling. Some parts of the code are adapdted from the implementation of diffpool.
For more details of the algorithm, please refer to our paper. If you find this work useful and use it in your research, please cite our paper.
@inproceedings{Ma:2019:GCN:3292500.3330982,
author = {Ma, Yao and Wang, Suhang and Aggarwal, Charu C. and Tang, Jiliang},
title = {Graph Convolutional Networks with EigenPooling},
booktitle = {Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
series = {KDD '19},
year = {2019},
isbn = {978-1-4503-6201-6},
location = {Anchorage, AK, USA},
pages = {723--731},
numpages = {9},
url = {http://doi.acm.org/10.1145/3292500.3330982},
doi = {10.1145/3292500.3330982},
acmid = {3330982},
publisher = {ACM},
address = {New York, NY, USA},
keywords = {graph classification, graph convolution networks, pooling, spectral graph theory},
}
Please check run_example.sh for an example of running the code.
You may download the preprocessed datasets here to save the time of preprocessing data.
Running on GPU may result in sub-optimal performance on some of the datasets inclduing ENZYMES, NCI1 and NCI109.