关键词: 表征学习, 动态异质图, 掩码自编码器, 自监督学习, 链路预测

Abstract: Real-world networks are often composed of multiple types of entities and interaction relationships, with topological structures and attributes evolving continuously over time. The heterogeneity and dynamics inherent in such networks were fully described by temporal heterogeneous graph. To alleviate the problems of coarse spatiotemporal information fusion and the heavy reliance on manual labels in existing temporal heterogeneous graph representation learning models, this paper proposes a masked autoencoder-enhanced temporal heterogeneous graph representation learning model. First, heterogeneous spatial information was fused through a multi-level attention structure, and temporal information was aggregated across snapshots. Then, representation information of nodes was enriched by leveraging the reconstruction loss of the masked autoencoder. Finally, improvements of 1%–4% in AUC (Area Under the Receiver Operating Characteristic Curve) over baseline models are achieved by the model on link prediction tasks across multiple real-world datasets. An effective self-supervised framework for temporal heterogeneous graph representation learning is provided by the model, facilitating more precise capture of heterogeneous information and dynamic evolution patterns in real networks.

Key words: representation learning, temporal heterogeneous graph, masked autoencoder, self-supervised learning, link prediction