This is a PyTorch implementation of Knowledge-inspired Reliable Distillation (KRD), and the code includes the following modules:

• Dataset Loader (Cora, Citeseer, Pubmed, Amazon-Photo, Coauthor-CS, Coauthor-Phy, and ogbn-arxiv)

• Two evaluation settings: transductive and inductive

• Various teacher GNN architectures (GCN, SAGE, GAT) and student MLPs

• Training paradigm for teacher GNNs and student MLPs

• Visualization and evaluation metrics

• numpy==1.21.6
• scipy==1.7.3
• torch==1.6.0
• dgl == 0.6.1
• sklearn == 1.0.2

• train_and_eval.py

  • train_teacher() -- Pre-train the teacher GNNs
  • train_student() -- Train the student MLPs with the pre-trained teacher GNNs

• models.py

  • MLP() -- student MLPs
  • GCN() -- GCN Classifier, working as teacher GNNs
  • GAT() -- GAT Classifier, working as teacher GNNs
  • GraphSAGE() -- GraphSAGE Classifier, working as teacher GNNs
  • Com_KD_Prob() -- Calculate knowledge reliability, model sampling probability, and updating power in a momentum manner

• dataloader.py

  • load_data() -- Load Cora, Citeseer, Pubmed, Amazon-Photo, Coauthor-CS, Coauthor-Phy, and ogbn-arxiv datasets

• utils.py

  • set_seed() -- Set radom seeds for reproducible results
  • graph_split() -- Split the data for the inductive setting

1. Install the required dependency packages

2. To get the results on a specific dataset with specific GNN as the teacher under a specific setting, please run with proper hyperparameters:

python train.py --dataset data_name --teacher gnn_name --exp_setting setting_name

where (1) data_name is one of the seven datasets: Cora, Citeseer, Pubmed, Amazon-Photo, Coauthor-CS, Coauthor-Phy ogbn-arxiv; (2) gnn_name is one of the three GNN architectures: GCN, SAGE, and GAT; (3) setting_name is one of the two evaluation settings: 0 (Transductive) and 1 (Inductive). Take the model in the transductive setting (with GCN as the teacher) on the Citeseer dataset as an example:

python main.py --dataset citeseer --teacher GCN --exp_setting 0

If you find this project useful for your research, please use the following BibTeX entry.

@inproceedings{wu2023quantifying,
  title={Quantifying the Knowledge in GNNs for Reliable Distillation into MLPs},
  author={Wu, Lirong and Lin, Haitao and Huang, Yufei and and Li, Stan Z},
  booktitle={International Conference on Machine Learning},
  year={2023},
  organization={PMLR}
}