ResVGAE: Going Deeper with Residual Modules for Link Prediction

TL;DR

ResVGAE introduces residual modules into deep variational graph autoencoders, enhancing their ability to capture multi-hop relations and outperforming existing models in link prediction tasks.

Contribution

This paper presents a novel deep graph autoencoder architecture with residual modules, improving multi-hop relation modeling in graph embeddings.

Findings

ResVGAE outperforms models without residual modules.

ResVGAE achieves comparable results to state-of-the-art methods.

Residual modules improve average precision in link prediction.

Abstract

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.