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.
arXiv cs.AI files mainly under ai research. We currently carry 1,128 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
inspect →
Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.22852 |
| Publication time | Mon, 25 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-25T04:07:35.648Z |
| Last seen | 2026-05-25T04:07:35.648Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | aZEq21gO6yl0 |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
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.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
Discussion
0 commentsMore from arXiv cs.AI
