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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)

Paper ID
2041
Venue
PODS
Year
2026
Pagerank
5.093636e-05
Overall Rank
10,166 | 30.26%
DOI
10.1145/3801911

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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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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 0.00059843817
2,600 Convergence of Datalog over (Pre-) Semirings 2022 PODS 8.3571843e-05
4,943 Datalog Unchained 2021 PODS 6.4332239e-05
5,893 A Query Language Perspective on Graph Learning 2023 PODS 6.0486927e-05
6,814 Expressive Power of Linear Algebra Query Languages 2021 PODS 5.7657129e-05
8,528 Aggregate Queries on Sparse Databases 2020 PODS 5.4119882e-05
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