An Ordinal Collaboration Network Model with Zero Truncated Poisson Latent Variables and Its Application

Yang, Qi; Tian, Yu-Zhu; Zhang, Yi-Jing; Wang, Yue; Mian, Zhibao

Abstract

Link prediction has traditionally been regarded as a binary classification problem, aiming to predict whether a link exists between two nodes in a given network. However, this binary framework fails to account for the cooperation intensity or the diversity of relationships. For example, in collaboration networks, the cooperation intensity often varies depending on the number of collaborations. Therefore, building on the premise of existing collaborations, this study models the relationships between authors as an ordinal multiclass problem to more accurately characterize varying levels of cooperation intensity. Then, the ordinal collaboration network model with zero-truncated Poisson latent variables (ZTP-OCN) is constructed. The maximum likelihood estimation (MLE) method is used to estimate the model parameters, and the performance of the model is evaluated by numerical simulation. Finally, this paper applies the ZTP-OCN model to the collaboration network of statistical journals to verify its validity in predicting the cooperation intensity. The results show that the model can describe the cooperation relationship with different intensity well.

Citation

Yang, Q., Tian, Y.-Z., Zhang, Y.-J., Wang, Y., & Mian, Z. (2025). An Ordinal Collaboration Network Model with Zero Truncated Poisson Latent Variables and Its Application. Stat, 14(1), Article e70040. https://doi.org/10.1002/sta4.70040

• Journal Article Type: Article
• Acceptance Date: Jan 5, 2025
• Online Publication Date: Jan 23, 2025
• Publication Date: Mar 1, 2025
• Deposit Date: Jan 13, 2025
• Publicly Available Date: Jan 24, 2026
• Journal: Stat
• Electronic ISSN: 2049-1573
• Publisher: John Wiley and Sons
• Peer Reviewed: Peer Reviewed
• Volume: 14
• Issue: 1
• Article Number: e70040
• DOI: https://doi.org/10.1002/sta4.70040
• Keywords: Cooperation intensity; Generalized latent variables; Maximum likelihood estimation; Ordinal collaboration network; Ordinal multiclassification
• Public URL: https://hull-repository.worktribe.com/output/5003469