Author

Tobias Käfer

2 papers indexed here

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

#machine learning Preprint Sep 2026

Edge-Level Automorphism in GNNs: A Quantitative Framework and Effective Designs For Link Prediction

A novel quantitative framework to assess GNN expressiveness for link prediction is introduced, and the edge automorphism ratio (EAR) is introduced, a scalar metric that quantifies a GNN's ability to distinguish links in a given graph.

Shao-Wei Chen, Donald Loveland, Tobias Käfer et al. · 0 citations

#graph neural networks Aug 2026

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.

Chen Shao, Yue Wang, Zhen-Yi Zhu et al. · Lecture notes in computer sc... · 1 citation