/AutoGCN

Primary LanguageJupyter NotebookMIT LicenseMIT

AutoGCN


1. Benchmark installation

Follow these instructions to install the benchmark and setup the environment.


2. Download datasets

Proceed as follows to download the benchmark datasets.


3. Training

gpu_id=0

model=AUTOGCN

python main_superpixels_graph_classification.py --config './configs/superpixels_graph_classification_'$model'_MNIST.json' --runs 5 --gpu_id $gpu_id

python main_superpixels_graph_classification.py --config './configs/superpixels_graph_classification_'$model'_CIFAR10.json' --runs 5 --gpu_id $gpu_id

python main_SBMs_node_classification.py --config './configs/SBMs_node_clustering_'$model'_CLUSTER.json' --runs 5 --gpu_id $gpu_id

python main_SBMs_node_classification.py --config './configs/SBMs_node_clustering_'$model'_PATTERN.json' --runs 5 --gpu_id $gpu_id

python main_molecules_graph_regression.py --config './configs/molecules_graph_regression_'$model'_ZINC.json' --runs 5 --gpu_id $gpu_id


4. Testing

gpu_id=0

python test_superpixel.py --config './configs/superpixels_graph_classification_AUTOGCN_MNIST.json' --model_path ./pretrained_models/AUTOGCN-MNIST.pkl --gpu_id $gpu_id

python test_superpixel.py --config './configs/superpixels_graph_classification_'AUTOGCN'_CIFAR10.json' --model_path ./pretrained_models/AUTOGCN-CIFAR.pkl --gpu_id $gpu_id

python test_sbm.py --config './configs/SBMs_node_clustering_AUTOGCN_CLUSTER.json' --model_path ./pretrained_models/AUTOGCN-CLUSTER.pkl --gpu_id $gpu_id

python test_sbm.py --config './configs/SBMs_node_clustering_AUTOGCN_CLUSTER.json' --model_path ./pretrained_models/AUTOGCN-PATTERN.pkl --gpu_id $gpu_id

python test_molecule.py --config './configs/molecules_graph_regression_AUTOGCN_ZINC.json' --model_path ./pretrained_models/AUTOGCN-ZINC.pkl --gpu_id $gpu_id


5. Ablation Study

cd experiments
sh autogcn-ab-cluster.sh
sh autogcn-ab-molecule.sh

6. Other experments

Other experiment scripts can be found at ./experiments folder


7. Acknowledgement

We thank Dwivedi et al. for providing the gnn benchmarking framework. Original repository of the gnn benchmarking framework can be found at https://github.com/graphdeeplearning/benchmarking-gnns.

Reference

@article{dwivedi2020benchmarkgnns,
  title={Benchmarking Graph Neural Networks},
  author={Dwivedi, Vijay Prakash and Joshi, Chaitanya K and Laurent, Thomas and Bengio, Yoshua and Bresson, Xavier},
  journal={arXiv preprint arXiv:2003.00982},
  year={2020}
}