  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. We design a k-fold model assessment
strategy for small datasets and a standard set of model training procedures for
all datasets, enabling a standard experimental pipeline for GNNs to help ensure
fair model architecture comparisons. We use node2vec and Laplacian eigenvectors
to perform data augmentation to investigate how input features affect the
performance of the models. We find topological information is important for
node classification tasks. Increasing the number of model layers does not
improve the performance except on the PATTERN and CLUSTER datasets, in which
the graphs are not connected. Data augmentation is highly useful, especially
using node2vec in the baseline, resulting in a substantial baseline performance
improvement.
