SHARQ: Explainability Framework for Association Rules on Relational Data
Summary: SHARQ quantifies an element’s contribution to relational association rules via Shapley values. Exact single-element SHARQ runs near-linear in rule count; a multi-element version amortizes cost, enabling rule- and attribute-importance. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Hadar Ben-Efraim
- 2. Susan B. Davidson
- 3. Amit Somech
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| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,449 | PY-SHARQ: A Holistic Python Library for Explaining Association Rules on Relational Data | 2025 | SIGMOD | 4.1945683e-05 |
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