Machine learning is gaining growing momentum in various recent models for the dynamic analysis of information flows in data communications networks. These preliminary models often rely on off-the-shelf learning models to predict from historical statistics while disregarding the physics governing the generating behaviors of these flows. This paper instead introduces Flow Neural Network (FlowNN) to improve the feature representation with learned physical bias. This is implemented by an induction layer, working upon the embedding layer, to impose the physics connected data correlations, and a self-supervised learning strategy with stop-gradient to make the learned physics universal. For the short-timescale network prediction tasks, FlowNN achieves 17% - 71% of loss decrease than the state-of-the-art baselines on both synthetic and real-world networking datasets, which shows the strength of this new approach.

翻译：机器学习在数据通信网络信息流动态分析的各类最新模型中日益受到重视。这些初步模型通常依赖现成的学习模型基于历史统计数据进行预测，却忽略了控制这些流生成行为的物理规律。本文提出流神经网络（FlowNN），通过引入学习的物理偏差来改进特征表示。该实现包括一个基于嵌入层工作的归纳层，用以施加物理相关的数据关联性，以及一个采用梯度停止策略的自监督学习机制，使学得的物理规律具有普适性。在短时网络预测任务中，FlowNN在合成数据集和真实网络数据集上均实现了比当前最优基线模型低17%至71%的损失降幅，充分展示了这一新方法的优势。