GOOD: A Graph Out-of-Distribution Benchmark. (arXiv:2206.08452v1 [cs.LG])

Web: http://arxiv.org/abs/2206.08452

Out-of-distribution (OOD) learning deals with scenarios in which training and
test data follow different distributions. Although general OOD problems have
been intensively studied in machine learning, graph OOD is only an emerging
area of research. Currently, there lacks a systematic benchmark tailored to
graph OOD method evaluation. In this work, we aim at developing an OOD
benchmark, known as GOOD, for graphs specifically. We explicitly make
distinctions between covariate and concept shifts and design data splits that
accurately reflect different …

arxiv benchmark distribution good graph lg