- Keywords: graph neural networks, graph classification, reproducibility, graph representation learning
- TL;DR: We provide a rigorous comparison of different Graph Neural Networks for graph classification.
- Abstract: The graph representation learning field has recently attracted the attention of a wide research community. Several Graph Neural Network models are being developed to tackle effective graph classification. However, experimental procedures often lack rigorousness and are hardly reproducible. Motivated by this, we provide an overview of common practices that should be avoided to fairly compare with the state of the art. To counter this troubling trend, we ran more than 47000 experiments in a controlled and uniform framework to re-evaluate five popular models across nine common benchmarks. Moreover, by comparing GNNs with structure-agnostic baselines we provide convincing evidence that, on some datasets, structural information has not been exploited yet. We believe that this work can contribute to the development of the graph learning field, by providing a much needed grounding for rigorous evaluations of graph classification models.
- Code: https://www.dropbox.com/s/b8x0dv2ozkomp1s/GNN_Evaluation.zip?dl=0