HeGAE-AC: Heterogeneous graph auto-encoder for attribute completion

Abstract

Heterogeneous Graphs (HGs) are widely used to model complex real-world networks that involve multiple types of nodes and relations. Many heterogeneous graph neural networks (HGNNs) based methods have been developed to learn low-dimensional representations for nodes in HGs with the assumption of complete attributes for all nodes. However, in many real applications, the attributes of certain nodes may be completely missing, leading to attribute-missing issues. Existing methods for attribute completion in HGs separate topological embedding learning from attribute completion, which can result in sub-optimal solutions. Moreover, the information of higher-order connected nodes is not fully exploited in the process of attribute completion. To address the issues mentioned above, we integrate topological embedding learning, attribute completion, and heterogeneous graph representation learning into an end-to-end framework called HeGAE-AC. Firstly, we utilize a heterogeneous graph auto-encoder (HeGAE) to incorporate the graph topology structure and attribute information of higher-order connected nodes into low-dimensional embeddings. Then, HeGAE decodes embeddings to generate attributes of all nodes. Finally, an HGNN model is employed to obtain node representations for downstream tasks using the completed HGs. We conduct extensive experiments on four real-world networks, which demonstrate that our approach achieves significant improvements over state-of-the-art methods.