Database Views as Explanations for Relational Deep Learning
Summary: Explains relational deep-learning predictions as concise database views, formalizing global abductive explanations via determinacy and tunable granularity. A hetero-GNN-specific learnable-mask method outperforms model-agnostic heuristics on RelBench in quality and efficiency. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Agapi Rissaki (Northeastern University)
- 2. Ilias Fountalis (RelationalAI)
- 3. Wolfgang Gatterbauer (Northeastern University)
- 4. Benny Kimelfeld (RelationalAI; Technion)
BibTeX Citation
@article{rissaki_vldb26,
title = {{Database Views as Explanations for Relational Deep Learning}},
author = {Rissaki, Agapi and Fountalis, Ilias and Gatterbauer, Wolfgang and Kimelfeld, Benny},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {7},
pages = {1643--1658},
doi = {10.14778/3801059.3801075},
url = {https://doi.org/10.14778/3801059.3801075},
year = {2026}
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