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Related Experiment Video

Updated: Mar 28, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

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Toward Robust End-to-End Delay Prediction: A GNN Approach With Routing-Aware Attention and Masked Subgraph Sampling.

Zichen Wang, Yiqi Chen, Dongwei Liu

    IEEE Transactions on Neural Networks and Learning Systems
    |March 26, 2026
    PubMed
    Summary

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    We developed a graph neural network (GNN) model for accurate end-to-end network delay prediction. This approach enhances intelligent network management by generalizing across various routing configurations.

    Area of Science:

    • Computer Science
    • Network Engineering

    Background:

    • Accurate end-to-end delay prediction is crucial for intelligent network management, especially in dynamic, latency-sensitive environments.
    • Existing deep learning (DL) models struggle with generalization due to their reliance on sequential routing path encoding.

    Purpose of the Study:

    • To propose a robust graph neural network (GNN)-based model for end-to-end delay prediction.
    • To overcome the generalization limitations of current DL models in network delay prediction.

    Main Methods:

    • A novel GNN model utilizing a global routing representation and a routing-aware attention mechanism.
    • Querying flow-relevant features from a unified topology-routing map, bypassing sequential routing information.
    • Employing a mask-based subgraph sampling strategy for inferring global routing correlations from partial flow interactions.

    Related Experiment Videos

    Last Updated: Mar 28, 2026

    Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
    05:30

    Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

    Published on: September 8, 2023

    1.2K

    Main Results:

    • The proposed GNN model demonstrates superior prediction accuracy compared to existing methods.
    • The model exhibits strong generalization capabilities across diverse and unseen routing configurations.
    • Experiments conducted on four public datasets (TnCwD, NSFNET, GBN, GEANT2) validate the model's performance.

    Conclusions:

    • The GNN-based delay prediction model offers a robust and adaptable solution for intelligent network management.
    • The approach effectively addresses the generalization limitations of previous DL models in network delay prediction.
    • Future research will explore model validation in more complex and dynamic network scenarios.