Graph neural networks (GNNs) have been widely used in various graph-related problems such as node classification and graph classification, where superior performance is mainly established when natural node features are available. However, it is not well understood how GNNs work without natural node features, especially regarding the various ways to construct artificial ones. In this paper, we point out the two types of artificial node features, i.e., positional and structural node features, and provide insights on why each of them is more appropriate for certain tasks, i.e., positional node classification, structural node classification, and graph classification. Extensive experimental results on 10 benchmark datasets validate our insights, thus leading to a practical guideline on the choices between different artificial node features for GNNs on non-attributed graphs. The code is available at https://github.com/zjzijielu/gnn-positional-structural-node-features.

翻译：图表神经网络(GNNs)被广泛用于各种与图表有关的问题,如节点分类和图表分类,其中主要在自然节点特征具备时确定优异性能,然而,人们并不清楚GNNs如何在没有自然节点特征的情况下工作,特别是没有构建人工节点的各种方法。在本文件中,我们指出了两类人工节点特征,即定位和结构节点特征,并深入说明了为什么每种类型都更适合某些任务,即定位节点分类、结构节点分类和图表分类。10个基准数据集的广泛实验结果证实了我们的洞察力,从而导致对非属性图形中GNNs不同人工节点特征之间的选择提出了实用准则。该代码可在https://github.com/zjzijielu/gnn-positional-strual-strual-node-fetatures查阅。该代码可在https://gthub.com/zzijielulu/gnn-stableal-stratral-node-fataties查阅。