Skip to content

An implementation of KDD paper "Graph Convolutional Networks with EigenPooling"

Notifications You must be signed in to change notification settings

tylersnetwork/eigenpooling

 
 

Repository files navigation

Graph Convolutional Networks with EigenPooling

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},
} 

Usage

Please check run_example.sh for an example of running the code.

Preprocessed datasets

You may download the preprocessed datasets here to save the time of preprocessing data.

Known Issue

Running on GPU may result in sub-optimal performance on some of the datasets inclduing ENZYMES, NCI1 and NCI109.

About

An implementation of KDD paper "Graph Convolutional Networks with EigenPooling"

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Python 99.4%
  • Shell 0.6%