3 papers
cs.LG2026
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…
cs.CC2025
Enumeration and updates for conjunctive linear algebra queries through expressibility
Thomas Muñoz, Cristian Riveros, Stijn Vansummeren
Due to the importance of linear algebra and matrix operations in data analytics, there is significant interest in using relational query optimization and processing techniques for…
cs.LG2025
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…