A smooth dynamic network model for patent collaboration data

TL;DR

This paper introduces a flexible semiparametric model for analyzing the evolution of inventor collaboration networks over time using patent data, accounting for external factors and network history.

Contribution

It develops a novel profile likelihood approach for modeling large-scale dynamic networks with time-stamped events, specifically applied to patent collaboration data.

Findings

Model effectively captures collaboration dynamics.

External and internal covariates influence inventor collaborations.

Applicable to large-scale patent networks.

Abstract

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.