4 citations · 4 across the 3 of their papers we have counts for
3 papers · 1 filter
On Halting vs Converging in Recurrent Graph Neural Networks
Jeroen Bollen, Stijn Vansummeren
Recurrent Graph Neural Networks (RGNNs) extend standard GNNs by iterating message-passing until some stopping condition is met. Various RGNN models have been proposed in the litera…
Halting Recurrent GNNs and the Graded -Calculus
Jeroen Bollen, Jan Van den Bussche, Stijn Vansummeren +1
Graph Neural Networks (GNNs) are a class of machine-learning models that operate on graph-structured data. Their expressive power is intimately related to logics that are invariant…
Learning Graph Neural Networks using Exact Compression
Jeroen Bollen, Jasper Steegmans, Jan Van den Bussche +1
Graph Neural Networks (GNNs) are a form of deep learning that enable a wide range of machine learning applications on graph-structured data. The learning of GNNs, however, is known…