Abstract
Citation Prediction, estimating whether paper a cites paper b, is particularly interesting in a forecasting setting where the model is trained on papers published before time t, and evaluated on papers published after h, where h is the forecast horizon. Performance improves with t (larger training sets) and degrades with h (longer forecast horizons). The trade-off between edge-based methods and node-based methods depends on t. Because edges grow faster than nodes, larger training sets favor edge-based methods.We introduce a new forecast-based Citation Prediction benchmark of 3 million papers to quantify these trends.Our benchmark shows that desirable policies for combining edge- and node-based methods depend on h and t.We release our benchmark, evaluation scripts, and embeddings.- Anthology ID:
- 2024.findings-emnlp.370
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2024
- Month:
- November
- Year:
- 2024
- Address:
- Miami, Florida, USA
- Editors:
- Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 6369–6388
- Language:
- URL:
- https://preview.aclanthology.org/icon-24-ingestion/2024.findings-emnlp.370/
- DOI:
- 10.18653/v1/2024.findings-emnlp.370
- Cite (ACL):
- Peter Vickers and Kenneth Church. 2024. Comparing Edge-based and Node-based Methods on a Citation Prediction Task. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 6369–6388, Miami, Florida, USA. Association for Computational Linguistics.
- Cite (Informal):
- Comparing Edge-based and Node-based Methods on a Citation Prediction Task (Vickers & Church, Findings 2024)
- PDF:
- https://preview.aclanthology.org/icon-24-ingestion/2024.findings-emnlp.370.pdf