On Positional and Structural Node Features for Graph Neural Networks on Non-attributed Graphs

• URL: http://arxiv.org/abs/2107.01495v1
• Date: Sat, 3 Jul 2021 20:37:26 GMT
• Title: On Positional and Structural Node Features for Graph Neural Networks on Non-attributed Graphs
• Authors: Hejie Cui, Zijie Lu, Pan Li, and Carl Yang
• Abstract summary: Graph neural networks (GNNs) have been widely used in various graph-related problems such as node classification and graph classification. 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.
• Score: 12.213147724959628
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph neural networks (GNNs) have been widely used in various graph-related problems such as node classification and graph classification, where the 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-exp/.

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