2 papers
cs.LG2026
Universality and Approximation Rates of Graph Neural Networks with Random Features
Lukas Gonon, Thilo Meyer-Brandis, Niklas Weber
We investigate message-passing graph neural networks with random node features. Random node features are known to enhance the expressiveness of graph neural networks (GNNs) both th…
q-fin.CP2024
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…