ResVGAE: Going Deeper with Residual Modules for Link Prediction
Machine Learning
2022-08-08 v2 Social and Information Networks
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.
Cite
@article{arxiv.2105.00695,
title = {ResVGAE: Going Deeper with Residual Modules for Link Prediction},
author = {Indrit Nallbani and Reyhan Kevser Keser and Aydin Ayanzadeh and Nurullah Çalık and Behçet Uğur Töreyin},
journal= {arXiv preprint arXiv:2105.00695},
year = {2022}
}