arXiv (All)
open access
EAGLE: Edge-Aware Graph Learning for Proactive Delivery Delay Prediction in Smart Logistics Networks
Modern logistics networks generate rich operational data streams at every warehouse node and transportation lane -- from order timestamps and routing records to shipping manifests -- yet predicting delivery delays remains predominantly reactive. Existing predictive approaches typically treat this pr
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- GNN4PPM: Multi-Target Predictive Process Monitoring with Relational Graph Convolutional Networks#1084479
- Gated Graph Attention Networks with Learnable Temperature#635852
- Gated Graph Attention Networks with Learnable Temperature#633503
- ADMETron: An AI-driven SaaS platform for comprehensive ADMET prediction and compound prioritisation#361570
- Training Graph Foundation Models on The Web Graph#1105453
Record · ID 1035454
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive