To address these challenges, we propose a Noise-Consistent hypeRgraph AutoEncoder framework with denoising strategies, termed NCRAE, aimed at achieving robust node embeddings in ceRNA regulatory ...

Autoencoders have been successfully used for graph embedding, and many variants have been proven to effectively express graph data and conduct graph analysis in low-dimensional space. However, ...

In this study, we propose MOLGAECL, a novel approach based on graph autoencoder pretraining and molecular graph contrastive learning. Initially, a large number of unlabeled molecular graphs are ...

AEGAE: Attribute-Embedded Graph Autoencoder This repository provides the implementation of the AEGAE method for community detection in attributed graphs. AEGAE integrates Laplacian regularization and ...

Then, we adopted an end-to-end framework to integrate graph autoencoder and inductive matrix completion, where the information from microbe and disease space are co-trained. Finally, the score matrix ...

This article puts forth a new training data-untethered model poisoning (MP) attack on federated learning (FL). The new MP attack extends an adversarial variational graph autoencoder (VGAE) to create ...

They transform the challenge of preserving structure information into maintaining inter-node similarity between the non-Euclidean, high-dimensional latent space and the Euclidean input space. For ...