A pipeline for fair comparison of graph neural networks in node classification tasks

Understand

Graph neural networks (GNNs) have been investigated for potential applicability in multiple fields that employ graph data.

• However, there are no standard training settings to ensure fair comparisons among new methods, including different model architectures and data augmentation techniques.
• We introduce a standard, reproducible benchmark to which the same training settings can be applied for node classification.
• For this benchmark, we constructed 9 datasets, including both small- and medium-scale datasets from different fields, and 7 different models.