Tobias Käfer

When GNNs Fail: Quantifying and Overcoming Temporal Correlation Volatility in Time Series

This work proposes Graph Layer for Inference in Dynamic En- vironments (GLIDE), a novel GNN layer enhanced by two theoretically grounded design mechanisms that significantly improve learning under dynamic topology while preserving robustness in static scenarios.