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cs.LG2025
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…
cs.LG2024
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…