Tag: Causal Graph Neural Networks
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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.
PBiLoss: Boost Fairness in Graph Recommender Systems
Discover how PBiLoss reduces popularity bias to enhance fairness and diversity in graph-based recommender systems without sacrificing accuracy.
MomentumGNN: Graph Neural Nets for Deformable Objects
Discover MomentumGNN, a graph neural network that conserves momentum for accurate deformable object simulations in graphics, robotics, and medical fields.
Multi-Agent Deep RL with Graph Neural Network Communication
Explore multi-agent deep reinforcement learning enhanced by graph neural network communication for improved coordination and performance.
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.