This repository contains the codebase for our research project titled "Graph Attention Networks for Biomedical Insights: MultiOmics Integration for Risk Stratification and Biomarker ...

The autoencoder consists of graph attention encoder and collaborative neural decoder, which is used to generate user and item latent vector accurately. And then we use the pairwise ranking learning ...

Unsupervised graph attention autoencoder clustering-oriented for community detection in attributed networks. community-detection representation-learning attributed-network graph-attention-autoencoder.

They are then individually fed into a multiview shared graph autoencoder, where clustering labels guide the learning of latent representations and the coefficient matrix. Furthermore, the proposed ...

Predicting potential microbe-disease associations with graph attention autoencoder, positive-unlabeled learning, and deep neural network Lihong Peng 1,2 † Liangliang Huang 1 † Geng Tian 3 Yan Wu 3 ...

The graph autoencoder adds the graph attention mechanism. It takes the constructed cell graph as input by adding different weights to different nodes, which can better capture cell relationships and ...

Recently, top-k recommendation system is getting more and more attention from researchers and unlike the rating prediction task, the purpose of top-k recommendation is to present the user with a list ...