Understand
Semi-supervised node classification in graphs is a fundamental problem in graph mining, and the recently proposed graph neural networks (GNNs) have achieved unparalleled results on this task.
- Due to their massive success, GNNs have attracted a lot of attention, and many novel architectures have been put forward.
- In this paper we show that existing evaluation strategies for GNN models have serious shortcomings.
- We show that using the same train/validation/test splits of the same datasets, as well as making significant changes to the training procedure (e.g.
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