Please give more details about the provenance of datasets about benchmarking-gnns HOT 1 CLOSED

Hi @DomInvivo, thank you for your interest and for your much-needed feedback; I appreciate it! Based on your comments, I agree that we can present the datasets better and intend to update the paper in the next version. In general, we have detailed descriptions of all datasets along with statistics available in Appendix A. Have you had a look at it and does it help answer some of your queries?

Re. ZINC:

• The constrained solubility property was selected based on pioneering work in molecule generation and our team's previous work. We will do our best to add more details in the updated paper.
• We chose only 12K random samples because we initially wanted to run our benchmarks quickly and efficiently. Indeed, it would be a good idea to make the full ZINC dataset available.

Re. CIFAR and MNIST:

• I believe we do mention our preprocessing technique in Appendix A. We actually follow this codebase. Let me know if more information is needed here.

Re. PATTERN and CLUSTER:

• I believe we have spoken about some of your queries in Appendix A. I agree, it would be a good idea to provide information on important graph properties such as the diameter and degree.
• In a nutshell, we can control the difficulty of these synthetic tasks via the Stochastic Block Model, which allows to control intra- and inter-community connectivity in the generative process. All graphs and sub-graphs (in PATTERN) are generated via SBMs, for which we give the exact configurations in the Appendix. Node features are assigned randomly.

I am happy to follow up and hear your feedback.

from benchmarking-gnns.