  Graph neural networks (GNNs) have been widely used in modeling graph
structured data, owing to its impressive performance in a wide range of
practical applications. Recently, knowledge distillation (KD) for GNNs has
enabled remarkable progress in graph model compression and knowledge transfer.
However, most of the existing KD methods require a large volume of real data,
which are not readily available in practice, and may preclude their
applicability in scenarios where the teacher model is trained on rare or hard
to acquire datasets. To address this problem, we propose the first end-to-end
framework for data-free adversarial knowledge distillation on graph structured
data (DFAD-GNN). To be specific, our DFAD-GNN employs a generative adversarial
network, which mainly consists of three components: a pre-trained teacher model
and a student model are regarded as two discriminators, and a generator is
utilized for deriving training graphs to distill knowledge from the teacher
model into the student model. Extensive experiments on various benchmark models
and six representative datasets demonstrate that our DFAD-GNN significantly
surpasses state-of-the-art data-free baselines in the graph classification
task.
