TaGSim: Type-aware Graph Similarity Learning and Computation
Summary: TaGSim enables type-aware GED estimation by modeling per-type effects for node/edge insertion, deletion, and relabeling with embeddings. A type-aware neural estimator aggregates inputs into GED estimates, outperforming prior methods on five datasets. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Jiyang Bai
- 2. Peixiang Zhao
Incoming Citations (Sorted by Pagerank)
Showing 5 of 5 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,752 | Computing Graph Edit Distance via Neural Graph Matching | 2023 | VLDB | 6.7879009e-05 |
| 8,069 | Computing Approximate Graph Edit Distance via Optimal Transport | 2025 | SIGMOD | 4.5934204e-05 |
| 10,236 | A Semantics-aware Approach for Graph Edit Distance Estimation over Knowledge Graphs | 2026 | VLDB | 4.1945683e-05 |
| 10,487 | Graph Edit Distance Estimation: A New Heuristic and A Holistic Evaluation of Learning-based Methods | 2025 | SIGMOD | 4.1945683e-05 |
| 10,692 | Fused Gromov-Wasserstein Alignment for Graph Edit Distance Computation and Beyond | 2025 | VLDB | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 1 of 1 cited papers.
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 |
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