Tag: Causal Graph Neural Networks
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PathMoG: Multi-Omics Graph Neural Network for Survival Prediction
Discover PathMoG, a pathway-centric graph neural network improving multi-omics cancer survival prediction with enhanced accuracy and interpretability.
Hysteresis Graph ODEs for Dynamic Topology-Feature Modeling
Discover Hysteresis Graph ODEs (HGODE) that improve graph neural networks by modeling coupled topology and feature evolution via continuous phase transitio...
Graph Neural Networks for Crystal Structure Prediction
Discover how graph neural networks optimize crystal structure prediction, accelerating material discovery with advanced combinatorial methods.
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.
Understanding GNNs’ Expressive Power with Global Readout
Explore how global readout enhances the expressive power of graph neural networks (GNNs) beyond traditional logic limits in this key research study.