2 citations · 4 across the 6 of their papers we have counts for
6 papers
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…
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…
Curvature-based Pooling within Graph Neural Networks
Cedric Sanders, Andreas Roth, Thomas Liebig
Over-squashing and over-smoothing are two critical issues, that limit the capabilities of graph neural networks (GNNs). While over-smoothing eliminates the differences between node…
Rank Collapse Causes Over-Smoothing and Over-Correlation in Graph Neural Networks
Andreas Roth, Thomas Liebig
Our study reveals new theoretical insights into over-smoothing and feature over-correlation in graph neural networks. Specifically, we demonstrate that with increased depth, node r…
Forecasting Unobserved Node States with spatio-temporal Graph Neural Networks
Andreas Roth, Thomas Liebig
Forecasting future states of sensors is key to solving tasks like weather prediction, route planning, and many others when dealing with networks of sensors. But complete spatial co…
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…