Juan L Reutter — accepted AI-conference papers

2025

How Expressive are Knowledge Graph Foundation Models?

Xingyue Huang, Pablo Barcelo, Michael M. Bronstein, Ismail Ilkan Ceylan, Mikhail Galkin, Juan L Reutter +1

ICML 2025poster

Knowledge Graph Foundation Models (KGFMs) are at the frontier for deep learning on knowledge graphs (KGs), as they can generalize to completely novel knowledge graphs with different relational vocabularies. Despite their empirical success, our theoretical understanding of KGFMs remains very limited.…

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2022

Expressiveness and Approximation Properties of Graph Neural Networks

Floris Geerts, Juan L Reutter

ICLR 2022oral

Characterizing the separation power of graph neural networks (GNNs) provides an understanding of their limitations for graph learning tasks. Results regarding separation power are, however, usually geared at specific GNNs architectures, and tools for understanding arbitrary GNN architectures are gen…

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2021

Graph Neural Networks with Local Graph Parameters

Pablo Barcelo, Floris Geerts, Juan L Reutter, Maksimilian Ryschkov

NeurIPS 2021poster

Various recent proposals increase the distinguishing power of Graph Neural Networks (GNNs) by propagating features between k-tuples of vertices. The distinguishing power of these “higher-order” GNNs is known to be bounded by the k-dimensional Weisfeiler-Leman (WL) test, yet their O(n^k) memory requi…

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