3 papers
cs.LG2026
The Boolean Power of ReLU
Pablo Barceló, Floris Geerts, Matthias Lanzinger +2
We prove that, on finite simple undirected graphs equipped with a single Boolean node feature, the Boolean queries expressible in -MPLang, for any collection of eventually c…
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…
cs.LG2024
Homomorphism Counts as Structural Encodings for Graph Learning
Linus Bao, Emily Jin, Michael Bronstein +2
Graph Transformers are popular neural networks that extend the well-known Transformer architecture to the graph domain. These architectures operate by applying self-attention on gr…