A ^VGAE: An Attribute-Augmented Adversarial Variational Graph Autoencoder for Link Prediction

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...

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Bibliographic Details
Published in:IEEE transactions on computational social systems pp. 1 - 16
Main Authors: Pan, Zhihong, Zhong, Zhijie, Wei, Lingling, Lin, Yunxuan, Li, Weisheng, Lin, Ronghua, Tang, Yong
Format: Journal Article
Language:English
Published: IEEE 16.06.2025
Subjects:
Adversarial mechanism
Autoencoders
Decoding
link prediction
Matrix decomposition
Network topology
Probabilistic logic
Recommender systems
scholar recommendation
Social networking (online)
Topology
Training
variational graph autoencoder (VGAE)
Vectors
ISSN:2329-924X, 2373-7476
Online Access:Get full text
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Summary: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<inline-formula><tex-math notation="LaTeX">{}^{3}</tex-math></inline-formula>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<inline-formula><tex-math notation="LaTeX">{}^{3}</tex-math></inline-formula>VGAE in link prediction tasks.
ISSN:2329-924X
2373-7476
DOI:10.1109/TCSS.2025.3569106