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ShapGraph: An Holistic View of Explanations through Provenance Graphs and Shapley Values

Summary: ShapGraph blends provenance graphs (global explanations) with Shapley-value-based tuple contributions (local explanations) for query results. An interactive GUI lets researchers switch between and combine views to identify influential data and how they interact to drive answers. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6385
Venue
SIGMOD
Year
2022
Pagerank
5.0666822e-05
Overall Rank
6,429 | 55.28%
DOI
10.1145/3514221.3520172

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 4 of 4 citing papers.

Rank Citing Paper Year Venue Pagerank
4,591 From Shapley Value to Model Counting and Back 2024 PODS 6.0619399e-05
5,916 Banzhaf Values for Facts in Query Answering 2024 SIGMOD 5.273953e-05
10,392 Shapley Value Estimation Based on Differential Matrix 2025 SIGMOD 4.1945683e-05
10,393 SHARQ: Explainability Framework for Association Rules on Relational Data 2025 SIGMOD 4.1945683e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 7 of 7 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
487 Why Not? 2009 SIGMOD 0.00022050218
1,119 The Complexity of Causality and Responsibility for Query Answers and non-Answers 2011 VLDB 0.0001386199
2,173 Querying Data Provenance 2010 SIGMOD 9.3676609e-05
2,256 ProvSQL: Provenance and Probability Management in PostgreSQL 2018 VLDB 9.1879032e-05
2,764 The Semiring Framework for Database Provenance 2017 PODS 8.1574444e-05
2,868 Computing the Shapley Value of Facts in Query Answering 2022 SIGMOD 7.9816425e-05
3,027 The Impact of Negation on the Complexity of the Shapley Value in Conjunctive Queries 2020 PODS 7.6842412e-05
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