A Longitudinal Ordinal Collaboration Network Model for Multiclass Link Prediction and Its Application

ABSTRACT With the growing application of collaboration networks in scientific research, link prediction has become an essential tool for understanding the structural and evolutionary mechanisms of academic relationships. Traditional link prediction methods typically model links as a binary classification problem, predicting whether a link exists between a pair of nodes. However, in real‐world networks, collaboration intensity varies, and simple 0–1 classification fails to capture such heterogeneity. To address this limitation, we propose a longitudinal ordinal collaboration network model for multiclass link prediction, which accounts for both the ordinal nature of collaboration intensity and the temporal evolution of collaborative relationships. Under the maximum likelihood framework, an regularization term is incorporated to enhance the stability and interpretability of parameter estimation. The proposed model extends link prediction from binary to ordinal multiclass classification and explicitly introduces a longitudinal structure to characterize the evolution of collaborations over time. Simulation studies and empirical analyses demonstrate that the model offers strong explanatory power and predictive performance, providing an effective tool for dynamic modelling of collaboration networks.