2026
Trimming the Fat: Redundancy-Aware Acceleration Framework for DGNNs
AAAI 2026technical
Temporal graphs are essential for modeling complex real-world systems, such as social interactions, financial transactions, and recommendation systems, but the high computational cost and model complexity of dynamic graph neural networks (DGNNs) pose significant challenges for practical deployment.