Graph Coordinates

Environments

Implementing environment: NVIDIA A100, 128GB (RAM)

Requirements

The PyTorch version we use is torch 1.13.1+cu117.

To install other requirements:

pip install -r requirements.txt

Preprocess+Training

To reproduce our results on OGB products and proteins datasets, please run the following commands. It will use random seeds from 0 to 9.

For ogbn-products:

For TCNN model:

python exampleRun_10randomSeed.py --config_file config_products_TC.json

For DVCNN model:

python exampleRun_10randomSeed.py --config_file config_products_DVC.json

For ogbn-proteins:

For TCNN model:

python exampleRun_10randomSeed.py --config_file config_proteins_TC.json

For DVCNN model:

python exampleRun_10randomSeed.py --config_file config_proteins_DVC.json

Node Classification Results:

Performance and number of parameters on ogbn-products:

Method	Params	Valid Accuracy	Test Accuracy
TCNN	22624	0.899089591±0.001062032	0.760623083±0.003675053
DVCNN	37039	0.872491926±0.001219387	0.718366484±0.001702856

Performance and number of parameters on ogbn-proteins:

Method	Params	Valid ROC-AUC	Test ROC-AUC
TCNN	22624	0.799306064±0.005573178	0.759988705±0.018049045
DVCNN	90608	0.825608641±0.005723252	0.791618037±0.008599669

Citing

If you find our work useful in your research, please consider citing our paper:

@inproceedings{qin2023graph,
  title={Graph Coordinates and Conventional Neural Networks-An Alternative for Graph Neural Networks},
  author={Qin, Zheyi and Paffenroth, Randy and Jayasumana, Anura P},
  booktitle={2023 IEEE International Conference on Big Data (BigData)},
  pages={4456--4465},
  year={2023},
  organization={IEEE}
}