Exploration Degree Bias: The Hidden Influence of Node Degree in Graph Neural Network-based Reinforcement Learning
P Tarábek, D Matis - IEEE Access, 2025 - ieeexplore.ieee.org
Graph Neural Networks (GNNs) have demonstrated remarkable performance in tasks
involving graph-structured data, but they also exhibit biases linked to node degrees. This …
involving graph-structured data, but they also exhibit biases linked to node degrees. This …
Spatial–Temporal Fusion Gated Transformer Network (STFGTN) for Traffic Flow Prediction
H Xie, X Fan, K Qi, D Wu, C Ren - Electronics, 2024 - mdpi.com
Traffic flow prediction is essential for smart city management and planning, aiding in
optimizing traffic scheduling and improving overall traffic conditions. However, due to the …
optimizing traffic scheduling and improving overall traffic conditions. However, due to the …
[PDF][PDF] Subgraph Federated Learning for Local Generalization
Federated Learning (FL) on graphs enables collaborative model training to enhance
performance without compromising the privacy of each client. However, previous methods …
performance without compromising the privacy of each client. However, previous methods …