Recursive Querying of Neural Networks via Weighted Structures
Summary: Develops recursive Datalog-like fixpoint logics for querying weighted structures, modeling unbounded-depth neural networks, with a novel overwrite (“loose”) semantics. Its scalar PTIME fragment captures all PTIME model-agnostic queries on reduced polynomial-weight networks, while unrestricted querying is already NP-complete. (summarized by gpt-5.6-luna on Jul 26 2026)
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Authors
- 1. Martin Grohe (RWTH Aachen University)
- 2. Christoph Standke (RWTH Aachen University)
- 3. Juno Steegmans (Hasselt University; University of Antwerp)
- 4. Jan Van den Bussche (Hasselt University)
BibTeX Citation
@inproceedings{grohe_pods26,
address = {New York, NY, USA},
series = {{PODS} '26},
title = {{Recursive Querying of Neural Networks via Weighted Structures}},
url = {https://dl.acm.org/doi/10.1145/3801911},
doi = {10.1145/3801911},
booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
publisher = {Association for Computing Machinery},
author = {Grohe, Martin and Standke, Christoph and Steegmans, Juno and Van den Bussche, Jan},
year = {2026}
}
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Outgoing Citations (Sorted by Pagerank)
Showing 6 of 6 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 17 | Provenance Semirings | 2007 | PODS | 0.00059813669 |
| 2,632 | Convergence of Datalog over (Pre-) Semirings | 2022 | PODS | 8.1988853e-05 |
| 5,070 | Datalog Unchained | 2021 | PODS | 6.2869206e-05 |
| 6,018 | A Query Language Perspective on Graph Learning | 2023 | PODS | 5.9101772e-05 |
| 6,946 | Expressive Power of Linear Algebra Query Languages | 2021 | PODS | 5.6356526e-05 |
| 8,705 | Aggregate Queries on Sparse Databases | 2020 | PODS | 5.2880532e-05 |
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