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IEEE1 a
RARE: Robust Masked Graph Autoencoder
To address this issue, we propose a novel SGP method termed Robust mAsked gRaph autoEncoder (RARE) to improve the certainty in inferring masked data and the reliability of the self-supervision ...
GitHub2 a
IEEE TKDE 2023-RARE: Robust Masked Graph Autoencoder
If you use this code for your research, please cite our paper. @article{2023RARE, title={RARE: Robust Masked Graph Autoencoder}, author={Wenxuan Tu and Qing Liao and Sihang Zhou and Xin Peng and Chuan ...
GitHub1 a
Evaluation of edges readiness using masked graph autoencoders
Welcome to the repository for our research project on Masked Graph Autoencoders ... Improving Understanding of Graph Structures: We investigate how MGM can enhance the capability of graph autoencoders ...
marktechpost1 a
Hierarchical Graph Masked AutoEncoders (Hi-GMAE): A Novel Multi-Scale GMAE Framework Designed to Handle the Hierarchical Structures within Graph
Current Generative graph-masked AutoEncoder (GMAE) models primarily concentrate on reconstructing node features, thereby capturing predominantly node-level information. This single-scale approach, ...
Frontiers2 a
Single-cell RNA-seq data analysis using graph autoencoders and graph attention networks
In this study, we developed scGAEGAT, a multi-modal model with graph autoencoders and graph attention networks ... cells at the single-cell level and sequence genes from uncommon or rare cells. The ...