SIGN: Scalable Inception Graph Networks [arXiv]

This repository contains the code to run the SIGN model on the ogbn-papers100M dataset, the largest, publicly available node classification benchmark at the time of writing. If you want to know more about SIGN checkout its arXiv page and its ICML 2020 GRL+ Workshop version.

Requirements

Dependencies with python 3.8.5:

torch==1.5.0
torch_geometric==1.6.1
torch_scatter==2.0.5
torch_sparse==0.6.7
ogb==1.2.3

Preprocessing, Training & Evaluation

# Generate SIGN features and save them as sign_333_embeddings.pt
python preprocessing.py --file_name sign_333_embeddings --undirected --directed --directed_asymm_norm --undirected_set_diag --directed_remove_diag

# Train SIGN model based on the preprocessed features generated above and write results at sign_results.txt
python sign_training.py --dropout 0.3 --lr 0.00005 --hidden_channels 512 --num_layers 3 --embeddings_file_name sign_333_embeddings.pt --result_file_name sign_results.txt

# Train SIGN-xl model based on the preprocessed features generated above and write results at sign-xl_results.txt
python sign_training.py --dropout 0.5 --lr 0.00005 --hidden_channels 2048 --num_layers 3 --embeddings_file_name sign_333_embeddings.pt --result_file_name sign-xl_results.txt

Cite us

@inproceedings{sign_icml_grl2020,
    title={SIGN: Scalable Inception Graph Neural Networks},
    author={Fabrizio Frasca and Emanuele Rossi and Davide Eynard and Benjamin Chamberlain and Michael Bronstein and Federico Monti},
    booktitle={ICML 2020 Workshop on Graph Representation Learning and Beyond},
    year={2020}
}