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)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
- 1. Mouyi Xu
- 2. Lijun Chang
Incoming Citations (Sorted by Pagerank)
Showing 0 of 0 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 3 of 3 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,099 | Comparing Stars: On Approximating Graph Edit Distance | 2009 | VLDB | 0.00012271234 |
| 3,407 | Computing Graph Edit Distance via Neural Graph Matching | 2023 | VLDB | 7.5100951e-05 |
| 3,859 | TaGSim: Type-aware Graph Similarity Learning and Computation | 2022 | VLDB | 7.119212e-05 |
Previous
Page 1 / 1
Next