  Existing knowledge distillation methods focus on convolutional neural
networks (CNNs), where the input samples like images lie in a grid domain, and
have largely overlooked graph convolutional networks (GCN) that handle non-grid
data. In this paper, we propose to our best knowledge the first dedicated
approach to distilling knowledge from a pre-trained GCN model. To enable the
knowledge transfer from the teacher GCN to the student, we propose a local
structure preserving module that explicitly accounts for the topological
semantics of the teacher. In this module, the local structure information from
both the teacher and the student are extracted as distributions, and hence
minimizing the distance between these distributions enables topology-aware
knowledge transfer from the teacher, yielding a compact yet high-performance
student model. Moreover, the proposed approach is readily extendable to dynamic
graph models, where the input graphs for the teacher and the student may
differ. We evaluate the proposed method on two different datasets using GCN
models of different architectures, and demonstrate that our method achieves the
state-of-the-art knowledge distillation performance for GCN models. Code is
publicly available at https://github.com/ihollywhy/DistillGCN.PyTorch.
