HAT-GAE: Self-supervised graph autoencoders with hierarchical adaptive masking and trainable corruption

Abstract

In recent years, self-supervised autoencoders have developed into a powerful representation learning framework in computer vision and natural language processing. However, their application to graph data has resulted in limited performance due to the non-Euclidean and complex structure of graphs compared to images or text, as well as the limitations of conventional autoencoder architectures. In this paper, we investigate factors impacting the performance of autoencoders on graph data and propose a novel autoencoder model for graph representation learning. Our model incorporates a hierarchical adaptive masking mechanism to incrementally increase the difficulty of training to mimic the process of human cognitive learning and a trainable corruption scheme to enhance the robustness of learned representations. Through extensive experimentation on ten benchmark datasets, we demonstrate the superiority of our proposed method over state-of-the-art graph representation learning models.