GLMAE: Graph Representation Learning Method Combining Generative Learning and Masking Autoencoder

Abstract

Graph representation learning is the foundation for various graph data mining tasks. In the real world, graph data not only contains complex adjacency relationships but also diverse structural information. To address issues such as overfitting and overemphasis on neighboring information while neglecting structural information in graph autoencoders, a novel approach that combines generative learning and masked autoencoder for graph representation learning is proposed. This method employs a masked autoencoder to mask a portion of the graph structure, using the remaining structure as input to the graph autoencoder, effectively alleviating overfitting. Additionally, leveraging generative learning theory, a new graph autoencoder is introduced, capable of aggregating both neighbor and structural information to generate high-quality graph embeddings. Comparative experiments between GLMAE and representative graph representation learning methods demonstrate that GLMAE achieves state-of-the-art performance in link prediction and node classification tasks.