HENCE-X: Toward Heterogeneity-agnostic Multi-level Explainability for Deep Graph Networks
Summary: HENCE-X: a heterogeneity-agnostic, causality-guided explainer for deep graph networks producing joint feature- and topology-level factual and counterfactual explanations. Theoretically guaranteed to recover the prediction's Markov blanket and empirically outperforms SOTA on real datasets. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Ge Lv
- 2. Chen Jason Zhang
- 3. Lei Chen
Incoming Citations (Sorted by Pagerank)
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| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,233 | Efficient GNN Training on Giant Graphs with Collective Batching and Scheduling | 2026 | VLDB | 4.1945683e-05 |
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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 |
|---|---|---|---|---|
| 3,600 | xFraud: Explainable Fraud Transaction Detection | 2022 | VLDB | 6.9315684e-05 |
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