Towards Pattern-aware Data Augmentation for Temporal Knowledge Graph Completion
Summary: Tackles entity/timestamp imbalance and model preferences in temporal KGC by introducing Booster, the first pattern-aware data augmentation that generates temporally and semantically consistent synthetic facts. Booster validates candidates via hierarchical triadic-closure scoring and uses two-stage, frequency-filtered training to mine hard samples and avoid false negatives, improving TKGC models by ~4.5%. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Jiasheng Zhang (Xidian University)
- 2. Deqiang Ouyang (Chongqing University)
- 3. Shuang Liang (University of Electronic Science and Technology of China)
- 4. Jie Shao (University of Electronic Science and Technology of China)
BibTeX Citation
@article{zhang_vldb25,
title = {{Towards Pattern-aware Data Augmentation for Temporal Knowledge Graph Completion}},
author = {Zhang, Jiasheng and Ouyang, Deqiang and Liang, Shuang and Shao, Jie},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {10},
pages = {3573--3586},
doi = {10.14778/3748191.3748216},
url = {https://doi.org/10.14778/3748191.3748216},
year = {2025}
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,944 | Real-Time Twitter Recommendation: Online Motif Detection in Large Dynamic Graphs | 2014 | VLDB | 9.4354517e-05 |
| 4,038 | BigST: Linear Complexity Spatio-Temporal Graph Neural Network for Traffic Forecasting on Large-Scale Road Networks | 2024 | VLDB | 6.9431109e-05 |
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