Entity Resolution with Hierarchical Graph Attention Networks
Summary: Proposes HierGAT, a hierarchical graph-attention ER model that jointly reasons about interdependent decisions, not pairwise. Uses graph attention to identify discriminative attributes/words, achieving up to 32.5% F1 over DeepMatcher and 8.7% over Ditto. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Dezhong Yao
- 2. Yuhong Gu
- 3. Gao Cong
- 4. Hai Jin
- 5. Xinqiao Lv
Incoming Citations (Sorted by Pagerank)
Showing 5 of 5 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,756 | Computing Graph Edit Distance via Neural Graph Matching | 2023 | VLDB | 6.781373e-05 |
| 9,240 | ThriftLLM: On Cost-Effective Selection of Large Language Models for Classification Queries | 2025 | VLDB | 4.3648789e-05 |
| 10,091 | LLM-Powered Interactive Graph Search: A Scalable and Practical Approach | 2026 | SIGMOD | 4.1905499e-05 |
| 11,050 | Blocker and Matcher Can Mutually Benefit: A Co-Learning Framework for Low-Resource Entity Resolution | 2024 | VLDB | 4.1905499e-05 |
| 11,062 | DARKER: Efficient Transformer with Data-driven Attention Mechanism for Time Series | 2024 | VLDB | 4.1905499e-05 |
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 15 of 15 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
Previous
Page 1 / 1
Next