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Tamara Drucks

2 accepted papers

2026

On the trade-off between expressivity and privacy in graph representation learning

ICLR 2026poster

We investigate the trade-off between expressive power and privacy guarantees in graph representation learning. Privacy-preserving machine learning faces growing regulatory demands that pose a fundamental challenge: safeguarding sensitive data while maintaining expressive power. To address this chall…

Cited by 0SourceScholar
2024

The Expressive Power of Path-Based Graph Neural Networks

ICML 2024poster

We systematically investigate the expressive power of path-based graph neural networks. While it has been shown that path-based graph neural networks can achieve strong empirical results, an investigation into their expressive power is lacking. Therefore, we propose PATH-WL, a general class of color…

Cited by 5SourcePDFScholar