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
- 2. Deqiang Ouyang
- 3. Shuang Liang
- 4. Jie Shao
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,228 | Real-Time Twitter Recommendation: Online Motif Detection in Large Dynamic Graphs | 2014 | VLDB | 9.2385241e-05 |
| 3,234 | BigST: Linear Complexity Spatio-Temporal Graph Neural Network for Traffic Forecasting on Large-Scale Road Networks | 2024 | VLDB | 7.3355287e-05 |
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