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Computing Graph Edit Distance via Neural Graph Matching

Summary: Introduce GEDGNN, a GNN that jointly predicts GED and a node-matching matrix, enabling recovery of edit paths via a k-best matching post-processing step—bridging regression-based GNNs and constructive edit-path algorithms. Achieves 4.9–74.3% MAE reduction vs prior GNNs and 53.6–88.1% MAE reduction on edit-path-derived GED vs state-of-the-art Noah across real and synthetic graphs. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
13039
Venue
VLDB
Year
2023
Pagerank
6.7879009e-05
Overall Rank
3,752 | 73.90%
DOI
10.14778/3594512.3594514

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Showing 4 of 4 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
3,001 Neural Subgraph Counting with Wasserstein Estimator 2022 SIGMOD 7.7404487e-05
3,778 A Learned Sketch for Subgraph Counting 2021 SIGMOD 6.7747398e-05
3,849 TaGSim: Type-aware Graph Similarity Learning and Computation 2022 VLDB 6.7064042e-05
4,837 Entity Resolution with Hierarchical Graph Attention Networks 2022 SIGMOD 5.8892326e-05
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