Search Results for author: Pál András Papp

Found 4 papers, 2 papers with code

Agent-based Graph Neural Networks

1 code implementation22 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.

Graph Classification Graph Neural Network

A Theoretical Comparison of Graph Neural Network Extensions

no code implementations30 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.

Graph Neural Network

DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks

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.

Graph Classification Graph Regression

Debt Swapping for Risk Mitigation in Financial Networks

no code implementations1 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.

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