  Predicting the number of coauthors for researchers contributes to
understanding the development of team science. However, it is an elusive task
due to diversity in the collaboration patterns of researchers. This study
provides a learning model for the dynamics of this variable; the parameters are
learned from empirical data that consist of the number of publications and the
number of coauthors at given time intervals. The model is based on relationship
between the annual number of new coauthors and time given an annual number of
publications, the relationship between the annual number of publications and
time given a historical number of publications, and Lotka's law. The
assumptions of the model are validated by applying it on the high-quality dblp
dataset. The effectiveness of the model is tested on the dataset by
satisfactory fittings on the evolutionary trend of the number of coauthors for
researchers, the distribution of this variable, and the occurrence probability
of collaboration events. Due to its regression nature, the model has the
potential to be extended to assess the confidence level of the prediction
results and thus has applicability to other empirical research.
