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
this https URL

本文介绍了两种构建人工节点特征的类型，包括位置和结构节点特征，并提供了对于每一种任务，即位置节点分类、结构节点分类和图分类，为什么每种节点特征更加适合的见解。此外，通过在 10 个基准数据集上进行广泛实验，验证了我们的洞察力，从而为非属性图上的 GNN 不同人工节点特征之间的选择提供了实用指南。