An Empirical Investigation of Shortcuts in Graph Learning

Deep learning has been extremely successful in a wide range of tasks in domains as diverse as image classification and natural language processing. However, at the same time, learning models may fail spectacularly, a phenomenon sometimes attributed to learning spurious correlations, or shortcuts, that deviate from the desired decision rule. In this paper, we perform an experimental analysis of the shortcut learning phenomenon on graphs, exposing the critical role played by the inductive bias of the learning model. Our results pave the way for a future principled theoretical analysis of this relevant phenomenon.