Graph Association Analyses for Early Drug Discovery
Summary: MedHunter builds a biomedical knowledge graph (DDKG) by integrating 11 biochemical sources with heterogeneous entity resolution, and supports incremental enrichment and cleaning. It introduces graph association rules (GARs)—graph-pattern rules that embed ML predicates—to discover drug–disease links, PPIs and DDIs and to drive extraction/cleaning. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Wenfei Fan (Beihang University; Shenzhen University; University of Edinburgh)
- 2. Daji Li (Shenzhen University)
- 3. Peiyu Liang (Shenzhen University)
- 4. Shuhao Liu (Shenzhen University)
- 5. Yaoshu Wang (Shenzhen University)
- 6. Yiming Wang (Shenzhen University)
- 7. Min Xie (Shenzhen University)
- 8. Runjie Zhang (Shenzhen University)
BibTeX Citation
@article{fan_vldb24,
title = {{Graph Association Analyses for Early Drug Discovery}},
author = {Fan, Wenfei and Li, Daji and Liang, Peiyu and Liu, Shuhao and Wang, Yaoshu and Wang, Yiming and Xie, Min and Zhang, Runjie},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {12},
pages = {4293--4296},
doi = {10.14778/3685800.3685858},
url = {https://doi.org/10.14778/3685800.3685858},
year = {2024}
}Incoming Citations (Sorted by Pagerank)
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Outgoing Citations (Sorted by Pagerank)
Showing 5 of 5 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,411 | word2vec, node2vec, graph2vec, X2vec: Towards a Theory of Vector Embeddings of Structured Data | 2020 | PODS | 0.00010852334 |
| 7,330 | Discovering Association Rules from Big Graphs | 2022 | VLDB | 5.6435171e-05 |
| 8,113 | Capturing Associations in Graphs | 2020 | VLDB | 5.484341e-05 |
| 8,225 | Deducing Certain Fixes to Graphs | 2019 | VLDB | 5.4624971e-05 |
| 9,627 | Making It Tractable to Catch Duplicates and Conflicts in Graphs | 2023 | SIGMOD | 5.2434488e-05 |
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