4 papers
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…
Grables: Tabular Learning Beyond Independent Rows
Tamara Cucumides, Floris Geerts
Tabular learning is still dominated by row-wise predictors that score each row independently, which fits i.i.d. benchmarks but fails on transactional, temporal, and relational tabl…
From Features to Structure: Task-Aware Graph Construction for Relational and Tabular Learning with GNNs
Tamara Cucumides, Floris Geerts
Tabular and relational data remain the most ubiquitous formats in real-world machine learning applications, spanning domains from finance to healthcare. Although both formats offer…
A note on the VC dimension of 1-dimensional GNNs
Noah Daniëls, Floris Geerts
Graph Neural Networks (GNNs) have become an essential tool for analyzing graph-structured data, leveraging their ability to capture complex relational information. While the expres…