8 citations · 53 across the 24 of their papers we have counts for
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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…
math.PR2026
On McKean-Vlasov SDEs with polynomial drifts for SIS epidemic models
Alexander Kalinin, Thilo Meyer-Brandis, Annika Steibel
We present a tractable class of one-dimensional McKean-Vlasov equations that allow for unique strong solutions and extend the dynamics of various SIS epidemic models that are well-…