Tag: graph representation learning
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Robust Learning on Heterogeneous Graphs with HGUL Framework
Discover the HGUL framework for robust learning on heterogeneous graphs with heterophily, tackling structural noise for improved graph representation.
Mini-Batch Bias Effects on GNN Link Prediction Accuracy
Discover how mini-batch class composition bias impacts GNN link prediction and learn strategies to improve graph neural network training.
Layer Embedding Deep Fusion GNN for Robust Graph Learning
Discover how Layer Embedding Deep Fusion GNN improves graph representation learning in low-homophily and heterophilic environments.
TACENR: Explainable Task-Agnostic Node Representations
Discover TACENR, a task-agnostic method offering contrastive explanations for node representations to enhance transparency in graph learning.
CoRe-ECG: Enhanced Self-Supervised Learning for 12-Lead ECG
Discover CoRe-ECG, a self-supervised method combining contrastive and reconstructive learning to improve 12-lead ECG analysis accuracy.