Coauthorship prediction applies predictive analytics to bibliographic data to predict authors who are highly likely to be coauthors. In this study, we propose an approach for coauthorship prediction based on bibliographic network embedding through a graph-based bibliographic data model that can be used to model common bibliographic data, including papers, terms, sources, authors, departments, research interests, universities, and countries. A real-world dataset released by AMiner that includes more than 2 million papers, 8 million citations, and 1.7 million authors were integrated into a large bibliographic network using the proposed bibliographic data model. Translation-based methods were applied to the entities and relationships to generate their low-dimensional embeddings while preserving their connectivity information in the original bibliographic network. We applied machine learning algorithms to embeddings that represent the coauthorship relationships of the two authors and achieved high prediction results. The reference model, which is the combination of a network embedding size of 100, the most basic translation-based method, and a gradient boosting method achieved an F1 score of 0.9 and even higher scores are obtainable with different embedding sizes and more advanced embedding methods. Thus, the strengths of the proposed approach lie in its customizable components under a unified framework.
|Number of pages||14|
|Journal||Journal of the Association for Information Science and Technology|
|Publication status||Accepted/In press - 2022|
Bibliographical noteFunding Information:
Yonsei University Research Fund, Grant/Award Number: 2022‐22‐01222 Funding information
This research was supported by the Yonsei University Research Fund of 2022 (2022‐22‐0122).
© 2022 Association for Information Science and Technology.
All Science Journal Classification (ASJC) codes
- Information Systems
- Computer Networks and Communications
- Information Systems and Management
- Library and Information Sciences