2 citations · 2 across the 2 of their papers we have counts for
4 papers · 1 filter
What Can We Learn From MIMO Graph Convolutions?
Andreas Roth, Thomas Liebig
Most graph neural networks (GNNs) utilize approximations of the general graph convolution derived in the graph Fourier domain. While GNNs are typically applied in the multi-input m…
Simplifying the Theory on Over-Smoothing
Andreas Roth
Graph convolutions have gained popularity due to their ability to efficiently operate on data with an irregular geometric structure. However, graph convolutions cause over-smoothin…
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…
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…