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 this https URL

本文提出了一种新颖的知识蒸馏方法，主要应用于非网格结构的数据，如图形结构数据。该方法采用了一种本地结构维护模块，从教师网络和学生网络中提取局部结构信息作为分布，最小化这些分布之间的距离，实现了拥有拓扑感知能力的知识传递，得到了一个更加轻量级但性能优异的学生网络。本文同时还将该方法应用于动态图模型，获得了在两个不同数据集上 GCN 模型的最新知识蒸馏性能。