We listed the details for reproducibility and additional experiments analysis here. The directory is as follows:

• Details for reproducibility:

  • Experimental environments.
  • The hyper-parameters used in LTD/FT.
  • The settings for teacher/student models and other distillation frameworks.
  • Some brief comments on data preparation.

• Additional experiments analysis:

  • Balance hyper-parameter analysis.
  • Performance under different training ratios.
  • Generalization gap analysis.
  • Original results of baselines.

• dgl==0.6.0
• Keras==2.4.3
• numpy==1.19.2
• optuna==2.6.0
• pandas==1.2.4
• python==3.8.8
• scikit-learn==0.19.2
• scipy==1.6.2
• sklearn==0.0
• tabulate==0.8.9
• torch==1.8.1
• torchvision==0.9.1

Because the A-Computers dataset is too large, we did not put it in the project file. The dataset can be downloaded from the following link and placed in the directory data/npz:

https://www.dropbox.com/s/26zd460xn4u6gmn/amazon_electronics_computers.npz?dl=0

Run student model:

python spawn_worker.py --dataset XXXX --teacher XXX --student XXX

For example, if you want train GCN on cora dataset:

python spawn_worker.py --dataset cora --teacher GCN --student GCN

We fix hyper-parameters setting as reported in our paper, and you also can use --automl to search hyper-parameters with the help of Optuna.