This repository is the official PyTorch implementation of "Implicit Graph Neural Networks".

Fangda Gu*, Heng Chang*, Wenwu Zhu, Somayeh Sojoudi, Laurent El Ghaoui, Implicit Graph Neural Networks, NeurIPS 2020.

The script has been tested running under Python 3.6.9, with the following packages installed (along with their dependencies):

• pytorch (tested on 1.6.0)
• torch_geometric (tested on 1.6.1)
• scipy (tested on 1.5.2)
• numpy (tested on 1.19.2)

We provide examples on the tasks of node classification and graph classification consistent with the experimental results of our paper. Please refer to nodeclassification and graphclassification for usage.

• If you find IGNN useful in your research, please cite the following in your manuscript:

@inproceedings{gu2020implicit,
 author = {Gu, Fangda and Chang, Heng and Zhu, Wenwu and Sojoudi, Somayeh and El Ghaoui, Laurent},
 booktitle = {Advances in Neural Information Processing Systems},
 pages = {11984--11995},
 publisher = {Curran Associates, Inc.},
 title = {Implicit Graph Neural Networks},
 volume = {33},
 year = {2020}
}