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cs.LG2026
Constant-Depth Rational-Weight GNNs and their Connection to Schanuel's Conjecture
Daumantas Kojelis, Carsten Lutz
A landmark result states that the expressive power of graph neural networks (GNNs) is bounded from above by the 1-dimensional Weisfeiler-Leman (1-WL) test. We ask whether a constan…
cs.DB2026
Neuro-Relational Programs: Unifying Queries and Neural Computation over Structured Data
Arie Soeteman, Balder ten Cate, Maurice Funk +3
The conventional approach to deep learning over relational databases applies neural models, such as Graph Neural Networks (GNNs), to a graph representation of the database. Recent…
cs.DB2026
Expressive Power of Deep Homomorphism Networks over Relational Databases
Moritz Schönherr, Balder ten Cate, Maurice Funk +3
The expressive limitations of message-passing Graph Neural Networks (GNNs) have motivated a wide range of more powerful graph learning architectures. We advocate Deep Homomorphism…