EAGLE: Edge-Aware Graph Learning for Proactive Delivery Delay Prediction in Smart Logistics Networks

arXiv:2604.05254 · cs.AI, cs.LG · Submitted 2026-04-06 · Read on arXiv

cs.AI, cs.LG

Submitted: 2026-04-06

Updated: 2026-04-06

DOI: 10.1145/3829441.3829533

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

Accuracy — 9× in this paper · explained in 1 episode
AUC — 8× in this paper · explained in 2 episodes
Transformer — 8× in this paper · explained in 2 episodes
Ablation — 3× in this paper · explained in 1 episode
Ablation study — 3× in this paper · explained in 1 episode
Aggregation — 3× in this paper · explained in 1 episode
GNNs — 3× in this paper · explained in 2 episodes
Regularization — 3× in this paper · explained in 3 episodes
Benchmark — 2× in this paper · explained in 2 episodes
Embeddings — 2× in this paper · explained in 2 episodes
Interpretability — 2× in this paper · explained in 15 episodes
Robustness — 2× in this paper · explained in 5 episodes
Trace — 2× in this paper · explained in 2 episodes
AdamW Optimizer — 1× in this paper · explained in 1 episode
Attention Mechanism — 1× in this paper · explained in 3 episodes
Calibration — 1× in this paper · explained in 7 episodes
Consistency — 1× in this paper · explained in 3 episodes
Core — 1× in this paper · explained in 2 episodes
Decoupling — 1× in this paper · explained in 2 episodes
Explainability — 1× in this paper · explained in 3 episodes
Generalization — 1× in this paper · explained in 13 episodes
Grounding — 1× in this paper · explained in 3 episodes

Sources

Resilient Routing: Risk-Aware Dynamic Routing in Smart Logistics via Spatiotemporal Graph Learning

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