AVGAE: An Attribute-Augmented Adversarial Variational Graph Autoencoder for Link Prediction

Zhihong Pan, Zhijie Zhong, Lingling Wei, Yunxuan Lin, Weisheng Li, Ronghua Lin, Yong Tang

Abstract: Link prediction is an important task that has numerous applications, including in recommender systems and social network analysis. Autoencoder is an effective method for solving the link prediction task. However, most existing autoencoder-based methods neither fully utilize the attribute information of the nodes nor fully take into account the potential data distribution in the graph. In this article, we propose a novel method named attribute-augmented adversarial variational graph autoencoder (A${}^{3}$VGAE), which can effectively solve the above two problems. The method first constructs the attribute structure graph based on the attribute information. Then, it inputs the topological structure graph, the attribute structure graph, and the attribute information into the shared encoder to obtain two latent representations. Besides, the topology structure graph, attribute structure graph, and attribute information are reconstructed by the dual decoder. The adversarial mechanism is introduced to ensure that the two latent representations match specific prior distributions. Extensive experiments conducted on four real-world graph datasets demonstrate the superiority of our proposed A${}^{3}$VGAE in link prediction tasks.

External IDs:dblp:journals/tcss/PanZWLLLT25