论文标题
使用图形神经网络分析MANETS的流量
Analyzing the Traffic of MANETs using Graph Neural Networks
论文作者
论文摘要
图形神经网络（GNN）在许多领域都在发挥作用，这要归功于它们在图形结构数据上的表现力。另一方面，随着网络技术已提高到5G水平，移动临时网络（MANETS）正在引起关注。但是，没有研究能够评估GNN在MANET上的效率。在这项研究中，我们旨在通过在流行的GNN框架（即Pytorch几何形状）中实施MANET数据集来填补这种缺席；并展示如何利用GNN来分析Manets的交通。我们使用图形（SAG）模型在数据集上操作一个边缘预测任务，其中SAG模型试图预测两个节点之间是否存在链接。我们将几个评估指标衡量，以衡量GNN在MANET上的性能和效率。 SAG模型在实验中平均显示出82.1精度。
Graph Neural Networks (GNNs) have been taking role in many areas, thanks to their expressive power on graph-structured data. On the other hand, Mobile Ad-Hoc Networks (MANETs) are gaining attention as network technologies have been taken to the 5G level. However, there is no study that evaluates the efficiency of GNNs on MANETs. In this study, we aim to fill this absence by implementing a MANET dataset in a popular GNN framework, i.e., PyTorch Geometric; and show how GNNs can be utilized to analyze the traffic of MANETs. We operate an edge prediction task on the dataset with GraphSAGE (SAG) model, where SAG model tries to predict whether there is a link between two nodes. We construe several evaluation metrics to measure the performance and efficiency of GNNs on MANETs. SAG model showed 82.1 accuracy on average in the experiments.
