Distilling Influences to Mitigate Prediction Churn in Graph Neural Networks

Andreas Roth, Thomas Liebig

Models with similar performances exhibit significant disagreement in the predictions of individual samples, referred to as prediction churn. Our work explores this phenomenon in gr…