1 code implementation • 22 Jun 2022 • Karolis Martinkus, Pál András Papp, Benedikt Schesch, Roger Wattenhofer
AgentNet is inspired by sublinear algorithms, featuring a computational complexity that is independent of the graph size.
no code implementations • 30 Jan 2022 • Pál András Papp, Roger Wattenhofer
We study and compare different Graph Neural Network extensions that increase the expressive power of GNNs beyond the Weisfeiler-Leman test.
1 code implementation • NeurIPS 2021 • Pál András Papp, Karolis Martinkus, Lukas Faber, Roger Wattenhofer
In DropGNNs, we execute multiple runs of a GNN on the input graph, with some of the nodes randomly and independently dropped in each of these runs.
Ranked #11 on Graph Classification on IMDb-B
no code implementations • 1 Jun 2021 • Pál András Papp, Roger Wattenhofer
We first show that there can be no positive swap for any pair of banks in a static financial system, or when a shock hits each bank in the network proportionally.