FSGNN

Implementation of FSGNN. For more details, please refer to our paper Experiments were conducted with following setup:
Pytorch: 1.6.0
Python: 3.8.5
Cuda: 10.2.89 Trained on NVIDIA V100 GPU.

Summary of results

To run node classification on different hops:

3-hop : ./run_classification_3_hop.sh

8-hop : ./run_classification_8_hop.sh

16-hop : ./run_classification_16_hop.sh

32-hop : ./run_classification_32_hop.sh

In addition, we include model accuracy of Actor dataset without using hop-normalization, as model shows higher accuracy in this setting.

Some more interesting results coming soon!

ogbn-papers100M (large-scale dataset)

Improved model than mentioned in paper with extra FC layer. Please run python process_large.py in folder named large_data first to create data splits.

Then run ./run_ogbn_papers.sh to train the model.

Accuracy shown below is average over five runs with random seeds 0-4.

(Results may vary slightly with a different platform, e.g. use of different GPU. In such case, for best performance, some hyperparameter search may be required. Please refer to PyTorch documentation for more details.)

Datasets and parts of preprocessing code were taken from Geom-GCN and GCNII repositories. We thank the authors of these papers for sharing their code.