From: NeurIPS.cc/2021/Workshop/DistShift/Program_Chairs
title: Paper Decision
decision: Accept (Spotlight)
comment: The paper curates a collection of graph datasets with distribution shifts, ranging from realistic shifts (e.g., generalizing to novel molecular scaffolds) to synthetic shifts (e.g., from stochastic block models). On these datasets, the authors show that existing domain generalization algorithms outperform ERM, which matches the findings of prior work on other modalities. They also show that graph augmentation techniques are promising, which is an interesting finding as data augmentation is not as well-developed for graphs as it is for other modalities. We believe this paper will be of interest to graph ML researchers as well as researchers studying distribution shifts in more generality.