In recent years, the powerful representation and modeling capabilities of graph neural networks for graph data have made them widely used in many fields and made breakthroughs. However, the existing models tend to optimize the graph convolution aggregation strategy and network structure and lack the exploration of the prior knowledge of the graph data itself. In response to the above problems, this paper designed a multi-teacher learning graph neural network based on feature information and structural information enhancement through the method of knowledge distillation, which broke the limitations of existing models for data prior knowledge extraction. Given the rich features and structural information behind the graph data, this paper designed the data enhancement methods of node feature and edge respectively. On this basis, knowledge embedding was performed on the original data and the enhanced data through the multi-teacher learning module, so that the student model could learn more prior knowledge about the data. On the Cora, Citeseer, and PubMed datasets, the node classification accuracy increased by 1%, 1.3% and 1.1%, respectively. Experimental results demonstrate that the information-augmented multi-teacher learning model proposed in this paper can effectively capture prior knowledge.

[2023-02-15] Accepted Paper /doc/2022.11.0765/v1

[2023-07-05] Printed Article /doc/2022.11.0765/v2