Graph Edit Distance Estimation: A New Heuristic and A Holistic Evaluation of Learning-based Methods
Summary: Holistic cross-field survey of learning-based GED predictors, separating interpretable and non-interpretable approaches and their design principles. Presents App-BMao, a simple, interpretable combinatorial GED estimator with bounded resources; on three datasets it outperforms all prior learning-based methods. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Mouyi Xu
- 2. Lijun Chang
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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 |
|---|---|---|---|---|
| 951 | Comparing Stars: On Approximating Graph Edit Distance | 2009 | VLDB | 0.00015106325 |
| 3,752 | Computing Graph Edit Distance via Neural Graph Matching | 2023 | VLDB | 6.7879009e-05 |
| 3,849 | TaGSim: Type-aware Graph Similarity Learning and Computation | 2022 | VLDB | 6.7064042e-05 |
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