3 papers
cs.LO2026
How Expressive Are Graph Neural Networks in the Presence of Node Identifiers?
Arie Soeteman, Michael Benedikt, Martin Grohe +1
Graph neural networks (GNNs) are a widely used class of machine learning models for graph-structured data, based on local aggregation over neighbors. GNNs have close connections to…
cs.LG2025
Logical Expressiveness of Graph Neural Networks with Hierarchical Node Individualization
Arie Soeteman, Balder ten Cate
We propose and study Hierarchical Ego Graph Neural Networks (HEGNNs), an expressive extension of graph neural networks (GNNs) with hierarchical node individualization, inspired by…
quant-ph2025
Non-zero noise extrapolation: accurately simulating noisy quantum circuits with tensor networks
Anthony P. Thompson, Arie Soeteman, Chris Cade +1
Understanding the effects of noise on quantum computations is fundamental to the development of quantum hardware and quantum algorithms. Simulation tools are essential for quantita…