2 citations · 2 across the 2 of their papers we have counts for
2 papers
cs.LG2023
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…
cs.LG2022★ 2 cited
Transforming PageRank into an Infinite-Depth Graph Neural Network
Andreas Roth, Thomas Liebig
Popular graph neural networks are shallow models, despite the success of very deep architectures in other application domains of deep learning. This reduces the modeling capacity a…