readme.md

Data-Free Adversarial Knowledge Distillation for Graph Neural Networks

• Paper link: https://arxiv.org/pdf/2205.03811
• Author's code repo: https://anonymous.4open.science/r/DF-GNNs-EC75

Dataset Statics

Dataset	# Graphs	# Nodes	# Edges	# Features	# Classes
MUTAG	188	~17.9	~39.6	7	2

Refer to TUDataset.

Results

TL_BACKEND="torch" python train_teacher.py --dataset MUTAG 
TL_BACKEND="torch" python train_student.py --dataset MUTAG --student gcn
TL_BACKEND="torch" python train_student.py --dataset MUTAG --student gin

Dataset	Student Model	Paper	Our(th)
MUTAG	gcn	76.2%	88.2%
MUTAG	gin	90.8%	88.2%
MUTAG	gat	79.5%	88.2%
MUTAG	graphsage	79.1%	88.2%