Expressive Power of Deep Homomorphism Networks over Relational Databases
The paper discusses the expressive power of Deep Homomorphism Networks (DHNs) in the context of relational databases. It establishes connections between DHNs and various fragments of first-order logic, highlighting their suitability for learning tasks related to SQL. Experimental results confirm that the differences in expressive power of DHNs are reflected in their performance on prediction tasks.
- ▪Deep Homomorphism Networks are proposed as a model for learning over relational databases.
- ▪The study relates DHNs to fragments of first-order logic and SQL.
- ▪Experiments show that the expressive power differences of DHNs impact their performance on prediction tasks.
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Computer Science > Databases arXiv:2605.22852 (cs) [Submitted on 18 May 2026] Title:Expressive Power of Deep Homomorphism Networks over Relational Databases Authors:Moritz Schönherr, Balder ten Cate, Maurice Funk, Benny Kimelfeld, Carsten Lutz, Arie Soeteman View a PDF of the paper titled Expressive Power of Deep Homomorphism Networks over Relational Databases, by Moritz Sch\"onherr and 5 other authors View PDF HTML (experimental) Abstract:The expressive limitations of message-passing Graph Neural Networks (GNNs) have motivated a wide range of more powerful graph learning architectures.
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