Representation Learning using Graph Autoencoders with Residual Connections.
Year: 2021
Type: preprint
Abstract: Graph autoencoders are very efficient at embedding graph-based complex data sets. However, most of the autoencoders have shallow depths and their efficiency tends to decrease with the increase of layer depth. In this paper, we study the effect of adding residual connections to shallow and deep graph variational and vanilla autoencoders. We show that residual connections improve the accuracy of the deep graph-based autoencoders. Furthermore, we propose Res-VGAE, a graph variational autoencoder with different residual connections. Our experiments show that our model achieves superior results when compared with other autoencoder-based models for the link prediction task.
Source: arXiv (Cornell University)
Institution: Istanbul Technical University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3158076488