33 citations · 67 across the 15 of their papers we have counts for
17 papers
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…
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…
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…
WL meet VC
Christopher Morris, Floris Geerts, Jan Tönshoff +1
Recently, many works studied the expressive power of graph neural networks (GNNs) by linking it to the -dimensional Weisfeiler--Leman algorithm (). Here, t…
Graph Neural Networks with Local Graph Parameters
Pablo Barceló, Floris Geerts, Juan Reutter +1
Various recent proposals increase the distinguishing power of Graph Neural Networks GNNs by propagating features between -tuples of vertices. The distinguishing power of these "…