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cs.LG2025
A Logical View of GNN-Style Computation and the Role of Activation Functions
Pablo Barceló, Floris Geerts, Matthias Lanzinger +2
We study the numerical and Boolean expressiveness of MPLang, a declarative language that captures the computation of graph neural networks (GNNs) through linear message passing and…
cs.LG2025
Message Passing on the Edge: Towards Scalable and Expressive GNNs
Pablo Barceló, Fabian Jogl, Alexander Kozachinskiy +3
Graph neural networks (GNNs) are widely used in graph learning and most architectures propagate information by passing messages between vertices. In this work, we shift our attenti…