论文标题
使用深门复发单元推断出复杂网络中代理转移的原始原始用途分布
Inferring origin-destination distribution of agent transfer in a complex network using deep gated recurrent units
论文作者
论文摘要
预测代理传输的原点污染（OD）概率分布是管理复杂系统的重要问题。但是，相关统计估计器的预测准确性不足。尽管已经提出了克服这种缺陷的特定技术，但仍然缺乏一般方法。在这里，我们提出了一个带有封闭式复发单元（DNNGRU）的深神经网络框架，以解决这一差距。我们的dnngru是\ emph {无网络}，因为它是通过监督学习训练的，并使用时间序列数据进行了有关通过边缘的代理量的数据。我们使用它来研究网络拓扑如何影响OD预测准确性，在这种情况下，观察到性能增强取决于不同OD所采用的路径之间的重叠程度。通过与给出确切结果的方法进行比较，我们证明了DNNGRU的近乎最佳性能，在不同的数据生成方案下，我们发现，我们发现，我们发现这始终优于现有方法和替代性神经网络体系结构。
Predicting the origin-destination (OD) probability distribution of agent transfer is an important problem for managing complex systems. However, prediction accuracy of associated statistical estimators suffer from underdetermination. While specific techniques have been proposed to overcome this deficiency, there still lacks a general approach. Here, we propose a deep neural network framework with gated recurrent units (DNNGRU) to address this gap. Our DNNGRU is \emph{network-free}, as it is trained by supervised learning with time-series data on the volume of agents passing through edges. We use it to investigate how network topologies affect OD prediction accuracy, where performance enhancement is observed to depend on the degree of overlap between paths taken by different ODs. By comparing against methods that give exact results, we demonstrate the near-optimal performance of our DNNGRU, which we found to consistently outperform existing methods and alternative neural network architectures, under diverse data generation scenarios.
