Review for NeurIPS paper: Open Graph Benchmark: Datasets for Machine Learning on Graphs

Weaknesses: - The contribution goes in several directions which makes the paper hard to evaluate; is the main contribution the selection of existing datasets, introducing new datasets or new versions of datasets, the empirical evaluation or the software tooling? - The dataset does not describe existing datasets and benchmarks, and so it is hard to judge the exact differences between the proposed datasets and currently used datasets. A more direct comparison might be useful, and it's not clear why existing, smaller datasets are not included in the collection. In particular, the datasets from Moleculenet seem to be mostly reproduced, using the splitting strategy that was suggested in their paper, with the modification potentially being addition of new features. What was the pool of datasets that was drawn from, and how were datasets selected? An example of such a work is the OpenML100 and OpenML CC-18 for classification, see Bischl et.