  The message-passing mechanism helps Graph Neural Networks (GNNs) achieve
remarkable results on various node classification tasks. Nevertheless, the
recursive nodes fetching and aggregation in message-passing cause inference
latency when deploying GNNs to large-scale graphs. One promising inference
acceleration direction is to distill the GNNs into message-passing-free student
multi-layer perceptrons (MLPs). However, the MLP student cannot fully learn the
structure knowledge due to the lack of structure inputs, which causes inferior
performance in the heterophily and inductive scenarios. To address this, we
intend to inject structure information into MLP-like students in low-latency
and interpretable ways. Specifically, we first design a Structure-Aware MLP
(SA-MLP) student that encodes both features and structures without
message-passing. Then, we introduce a novel structure-mixing knowledge
distillation strategy to enhance the learning ability of MLPs for structure
information. Furthermore, we design a latent structure embedding approximation
technique with two-stage distillation for inductive scenarios. Extensive
experiments on eight benchmark datasets under both transductive and inductive
settings show that our SA-MLP can consistently outperform the teacher GNNs,
while maintaining faster inference as MLPs. The source code of our work can be
found in https://github.com/JC-202/SA-MLP.
