Christian Nauck

Deep GNNs can achieve superior depth scaling and accuracy by leveraging controlled Turing instability or stable near-critical propagation to combat over-smoothing.

Explicitly modeling the temporal rate of change in spatio-temporal graph models unlocks superior prediction accuracy and pattern preservation for propagating phenomena.

Power systems could become the gold standard for testing graph machine learning, yet face a reproducibility crisis due to scarce benchmarks and datasets.