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Deep Homomorphism Networks show expressive power over relational databases

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced Deep Homomorphism Networks (DHNs) as a powerful architecture for learning from relational databases, drawing parallels to fragments of SQL. Their study connects DHNs with various extensions of first-order logic, including those with counting and ratio quantifiers. These findings also shed light on the decidability of static analysis problems for DHNs and are supported by experimental results showing performance differences that align with their expressive power. AI

IMPACT Introduces a new model architecture with theoretical connections to SQL, potentially improving database learning tasks.

RANK_REASON The cluster contains an academic paper detailing a new model architecture and its theoretical properties. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Deep Homomorphism Networks show expressive power over relational databases

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The cluster contains an academic paper detailing a new model architecture and its theoretical properties. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Moritz Sch\"onherr, Balder ten Cate, Maurice Funk, Benny Kimelfeld, Carsten Lutz, Arie Soeteman ·

    Expressive Power of Deep Homomorphism Networks over Relational Databases

    arXiv:2605.22852v1 Announce Type: cross Abstract: 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 Networks (DHNs) as a model particularly well-suite…

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