论文标题
用于Internet路由数据的基准图形神经网络
Benchmarking Graph Neural Networks for Internet Routing Data
论文作者
论文摘要
Internet由称为自主系统（或ASE）的网络组成，相互关联，从而形成了一个大图。虽然均已知道的媒介物均已知道，并且有许多可用于ASE的数据（即节点属性），但在Internet数据上应用图形机器学习（ML）方法的研究并没有引起很多关注。在这项工作中，我们提供了一个基准测试框架，旨在使用Graph-ML和Graph Neural Network（GNN）方法促进Internet数据的研究。具体来说，我们通过从多个在线来源收集数据并进行预处理，以便可以轻松地将它们用作GNN架构中的输入，从而通过收集多个在线来源来编译具有异质节点/作为属性的数据集。然后，我们创建一个用于在编译数据上应用GNN的框架/管道。对于一组任务，我们对不同的GNN模型（以及非GNN ML模型）进行基准测试以测试其效率。我们的结果可以作为未来研究的常见基线，并为GNN在互联网数据上的应用提供初步见解。
The Internet is composed of networks, called Autonomous Systems (or, ASes), interconnected to each other, thus forming a large graph. While both the AS-graph is known and there is a multitude of data available for the ASes (i.e., node attributes), the research on applying graph machine learning (ML) methods on Internet data has not attracted a lot of attention. In this work, we provide a benchmarking framework aiming to facilitate research on Internet data using graph-ML and graph neural network (GNN) methods. Specifically, we compile a dataset with heterogeneous node/AS attributes by collecting data from multiple online sources, and preprocessing them so that they can be easily used as input in GNN architectures. Then, we create a framework/pipeline for applying GNNs on the compiled data. For a set of tasks, we perform a benchmarking of different GNN models (as well as, non-GNN ML models) to test their efficiency; our results can serve as a common baseline for future research and provide initial insights for the application of GNNs on Internet data.
