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GNNetworkingChallenge:图神经网络挑战的 RouteNet 基线 (https)
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2021-05-29
20:12:13
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RouteNet - 图神经网络挑战 2020 作为“ITU AI/ML in 5G 挑战”的一部分而组织 挑战网站: : 联系邮件列表: (您首先需要订阅: )。 是一种新颖的图神经网络 (GNN) 模型,被提议作为一种经济高效的替代方案,用于估计网络中每个源目的地的性能指标(例如,延迟、抖动、损失)。 由于其在图形结构数据上运行的 GNN 架构,RouteNet 展示了一种前所未有的能力,可以学习和建模网络中拓扑、路由和输入流量之间的复杂关系。 因此,即使在训练期间看不到的网络场景中,它也能够以与资源匮乏的数据包级模拟器相似的准确度进行性能预测。 这为网络运营商提供了一种功能工具,能够准确预测端到端的关键性能指标(例如,延迟、抖动、丢失)。 快速开始 要求 我们强烈建议使用 Python 3.7,因为较低版本的 Python 可能会导致定义所有 PATH 环境变量时出现问题。
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GNNetworkingChallenge:图神经网络挑战的 RouteNet 基线 (https) (1223个子文件)
gnnet_data_set_300_399.tar.gz 1.61MB
gnnet_data_set_100_199.tar.gz 1.52MB
gnnet_data_set_0_99.tar.gz 1.49MB
gnnet_data_set_200_299.tar.gz 1.48MB
gnnet_data_set_100_199.tar.gz 1.14MB
gnnet_data_set_0_99.tar.gz 1.13MB
gnnet_data_set_100_199.tar.gz 590KB
gnnet_data_set_0_99.tar.gz 581KB
config.ini 1KB
LICENSE 13KB
README.md 12KB
routenet_scheme.PNG 11KB
datanetAPI.py 32KB
routenet_model.py 12KB
read_dataset.py 6KB
main.py 6KB
generate_submission.py 4KB
1.py 134B
1.py+ 116B
graph-geant2-wfq-51.txt 10KB
graph-geant2-wfq-97.txt 10KB
graph-geant2-wfq-81.txt 10KB
graph-geant2-wfq-4.txt 10KB
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