ResVGAE: Going Deeper with Residual Modules for Link Prediction
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Abstract (arXiv)
Graph autoencoders are efficient at embedding graph-based data sets. Most graph autoencoder architectures have shallow depths which limits their ability to capture meaningful relations between nodes separated by multi-hops. In this paper, we propose Residual Variational Graph Autoencoder, ResVGAE, a deep variational graph autoencoder model with multiple residual modules. We show that our multiple residual modules, a convolutional layer with residual connection, improve the average precision of the graph autoencoders. Experimental results suggest that our proposed model with residual modules outperforms the models without residual modules and achieves similar results when compared with other state-of-the-art methods.
Key facts. Cited by 2 papers in the map (data through 13 Oct 2026). Builds on 20 of its 31 references that are in the map. First posted on arXiv 3 May 2021.
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20 of 31 references.
References · 20
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8 more
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.