Please add the 10 smaller datasets from MoleculeNet to the benchmarks. They are ogbg-moltox21, ogbg-molbace, ogbg-molbbbp, ogbg-molclintox, ogbg-molmuv, ogbg-molsider, and ogbg-moltoxcast for (multi-task) binary classification, and ogbg-molesol, ogbg-molfreesolv, and ogbg-mollipo for regression.
See https://ogb.stanford.edu/docs/graphprop/
As the manual selection of parameters for a graph neural network is difficult, please add support
for some of the automated machine learning techniques.
See for example techniques described in AutoGL
Dear Graphormer authors,
thanks for this great piece of software! I have some feature requests.
Can you please add the functionality for evidential deep learning? See article: ACS Cent. Sci. 2021, 7, 8, 1356–1367
Please add the 10 smaller datasets from MoleculeNet to the benchmarks. They are ogbg-moltox21, ogbg-molbace, ogbg-molbbbp, ogbg-molclintox, ogbg-molmuv, ogbg-molsider, and ogbg-moltoxcast for (multi-task) binary classification, and ogbg-molesol, ogbg-molfreesolv, and ogbg-mollipo for regression. See https://ogb.stanford.edu/docs/graphprop/
Please add functionality for molecular representation pre-training via attribute masking See Strategies for Pre-training Graph Neural Networks
Please add metrics described in the Regression Metrics Guide
As the manual selection of parameters for a graph neural network is difficult, please add support for some of the automated machine learning techniques. See for example techniques described in AutoGL
Many thanks.