Modelling the large and dynamically growing bipartite network of German patents and inventors

TL;DR

This paper models the large, evolving bipartite network of German inventors and patents in electrical engineering, introducing a novel temporal ERGM approach to analyze innovation dynamics.

Contribution

It develops a bipartite variant of the Temporal Exponential Random Graph Model that accounts for actor turnover and pairwise inventor statistics in large dynamic networks.

Findings

Inventor characteristics influence innovation activity

Knowledge flows significantly impact patent collaborations

The model effectively captures network evolution over time

Abstract

We analyse the bipartite dynamic network of inventors and patents registered within the main area of electrical engineering in Germany to explore the driving forces behind innovation. The data at hand leads to a bipartite network, where an edge between an inventor and a patent is present if the inventor is a co-owner of the respective patent. Since more than a hundred thousand patents were filed by similarly as many inventors during the observational period, this amounts to a massive bipartite network, too large to be analysed as a whole. Therefore, we decompose the bipartite network by utilising an essential characteristic of the network: most inventors tend to stay active only for a relatively short period, while new ones become active at each point in time. Consequently, the adjacency matrix carries several structural zeros. To accommodate for these, we propose a bipartite variant of the Temporal Exponential Random Graph Model (TERGM) in which we let the actor set vary over time, differentiate between inventors that already submitted patents and those that did not, and account for pairwise statistics of inventors. Our results corroborate the hypotheses that inventor characteristics and knowledge flows play a crucial role in the dynamics of invention.