Computing Approximate Graph Edit Distance via Optimal Transport
Summary: Uses optimal transport to approximate graph edit distance by deriving vertex coupling from the cost matrix via inverse OT with a learnable Sinkhorn (GEDIOT). Proposes GEDGW (OT + Gromov–Wasserstein) and GEDHOT, a fusion of GEDIOT and GEDGW for improved GED accuracy and generalization. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Qihao Cheng (Tsinghua University)
- 2. Da Yan (Indiana University)
- 3. Tianhao Wu (Tsinghua University)
- 4. Zhongyi Huang (Tsinghua University)
- 5. Qin Zhang (Indiana University)
BibTeX Citation
@inproceedings{cheng_sigmod25,
title = {{Computing Approximate Graph Edit Distance via Optimal Transport}},
author = {Cheng, Qihao and Yan, Da and Wu, Tianhao and Huang, Zhongyi and Zhang, Qin},
series = {{SIGMOD} '25},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3709673},
url = {https://dl.acm.org/doi/10.1145/3709673},
year = {2025}
}Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 9,235 | Fused Gromov-Wasserstein Alignment for Graph Edit Distance Computation and Beyond | 2025 | VLDB | 5.2056825e-05 |
| 10,708 | A Semantics-aware Approach for Graph Edit Distance Estimation over Knowledge Graphs | 2026 | VLDB | 4.9793485e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 8 of 8 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,133 | Comparing Stars: On Approximating Graph Edit Distance | 2009 | VLDB | 0.00011898162 |
| 2,634 | Neural Subgraph Counting with Wasserstein Estimator | 2022 | SIGMOD | 8.1993804e-05 |
| 3,160 | A Learned Sketch for Subgraph Counting | 2021 | SIGMOD | 7.5807496e-05 |
| 3,453 | Computing Graph Edit Distance via Neural Graph Matching | 2023 | VLDB | 7.2904632e-05 |
| 3,569 | A Partition-Based Approach to Structure Similarity Search | 2014 | VLDB | 7.1987748e-05 |
| 3,994 | TaGSim: Type-aware Graph Similarity Learning and Computation | 2022 | VLDB | 6.8678851e-05 |
| 5,998 | Neural Attributed Community Search at Billion Scale | 2023 | SIGMOD | 5.9198696e-05 |
| 7,206 | Boosting Graph Similarity Search through Pre-Computation | 2021 | SIGMOD | 5.5862482e-05 |
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| 2 | 1,133 | Comparing Stars: On Approximating Graph Edit Distance | 2009 | VLDB |
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| 4 | 12,059 | Approximate Pattern Matching in Massive Graphs with Precision and Recall Guarantees | 2020 | SIGMOD |
| 5 | 10,708 | A Semantics-aware Approach for Graph Edit Distance Estimation over Knowledge Graphs | 2026 | VLDB |
| 6 | 3,994 | TaGSim: Type-aware Graph Similarity Learning and Computation | 2022 | VLDB |
| 7 | 12,316 | NED: An Inter-Graph Node Metric Based On Edit Distance | 2017 | VLDB |
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