GraphWorld: Fake Graphs Bring Real Insights for GNNs

Despite advances in the field of Graph Neural Networks (GNNs), only a small number ( 5) of datasets are currently used to evaluate new models. This continued reliance on a handful of datasets provides minimal insight into the performance differences between models, and is especially challenging for industrial practitioners who are likely to have datasets which are very different from academic benchmarks. In the course of our work on GNN infrastructure and open-source software at Google, we have sought to develop benchmarks that are robust, tunable, scalable, and generalizable.