On Data-Aware Global Explainability of Graph Neural Networks
Summary: DAG-Explainer: data-aware global GNN explanations optimizing model-faithfulness, data-distribution compliance, and class discriminativity. NP-hard; a randomized greedy algorithm with improved approximation bound and competitive empirical fidelity. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
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Incoming Citations (Sorted by Pagerank)
Showing 4 of 4 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 9,400 | Explaining GNN-based Recommendations in Logic | 2025 | VLDB | 4.3441378e-05 |
| 10,015 | Differentially Private Explanations for Clusters | 2026 | SIGMOD | 4.1945683e-05 |
| 10,233 | Efficient GNN Training on Giant Graphs with Collective Batching and Scheduling | 2026 | VLDB | 4.1945683e-05 |
| 10,269 | Database Views as Explanations for Relational Deep Learning | 2026 | VLDB | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 5 of 5 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,119 | The Complexity of Causality and Responsibility for Query Answers and non-Answers | 2011 | VLDB | 0.0001386199 |
| 1,867 | Interpretable Data-Based Explanations for Fairness Debugging | 2022 | SIGMOD | 0.00010272055 |
| 2,649 | Explaining Query Answers with Explanation-Ready Databases | 2016 | VLDB | 8.3719123e-05 |
| 6,565 | Toward Interpretable and Actionable Data Analysis with Explanations and Causality | 2022 | VLDB | 5.0081626e-05 |
| 7,556 | Interactive Query Explanations Using Fine Grained Provenance | 2022 | SIGMOD | 4.7117814e-05 |
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