The development and application of models, which take the evolution of network dynamics into account are receiving increasing attention. We contribute to this field and focus on a profile likelihood approach to model time-stamped event data for a large-scale dynamic network. We investigate the collaboration of inventors using EU patent data. As event we consider the submission of a joint patent and we explore the driving forces for collaboration between inventors. We propose a flexible semiparametric model, which includes external and internal covariates, where the latter are built from the network history.
@article{arxiv.1909.00736,
title = {A smooth dynamic network model for patent collaboration data},
author = {Verena Bauer and Dietmar Harhoff and Göran Kauermann},
journal= {arXiv preprint arXiv:1909.00736},
year = {2020}
}
Comments
Major change: We had a discrepancy in the implementation and the notation in the paper of the covariate vector. Further changes: Wordings and combinations of some figures