approximation theory 1graph neural networks 1message passing 1permutation equivariance 1random features 1universality 1
From the 1 of 2 linked papers with an AI index.
2 papers
cs.LG2026
Universality and Approximation Rates of Graph Neural Networks with Random Features
Lukas Gonon, Thilo Meyer-Brandis, Niklas Weber
The paper proves that message‑passing graph neural networks with partially random node features can universally approximate any permutation‑invariant or equivariant function on fix…
q-fin.CP2025
Computing Systemic Risk Measures with Graph Neural Networks
Lukas Gonon, Thilo Meyer-Brandis, Niklas Weber
This paper investigates systemic risk measures for stochastic financial networks of explicitly modelled bilateral liabilities. We extend the notion of systemic risk measures from B…