46 citations · 122 across the 19 of their papers we have counts for
20 papers
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…
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…
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…
Representing Paths in Graph Database Pattern Matching
Wim Martens, Matthias Niewerth, Tina Popp +2
Modern graph database query languages such as GQL, SQL/PGQ, and their academic predecessor G-Core promote paths to first-class citizens in the sense that paths that match regular p…
CORE: a Complex Event Recognition Engine
Marco Bucchi, Alejandro Grez, Andrés Quintana +2
Complex Event Recognition (CER) systems are a prominent technology for finding user-defined query patterns over large data streams in real time. CER query evaluation is known to be…