Chao Gao

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.

Swapping internal activations can dramatically improve agreement in model predictions across different question framings.