Modeling the Large and Dynamically Growing Bipartite Network of German Patents and Inventors

• Activity Number: 432 - Contributed Poster Presentations: Royal Statistical Society
• Type: Contributed
• Date/Time: Wednesday, August 10, 2022 : 10:30 AM to 12:20 PM
• Sponsor: Royal Statistical Society
• Author(s): Cornelius Fritz* and Göran Kauermann and Giacomo De Nicola and Sevag Kevork and Dietmar Harhoff
• Companies: LMU Munich and LMU Munich and LMU Munich and LMU Munich and Max Planck Institute for Innovation and Competition
• Keywords: Bipartite networks; Patent collaboration; Temporal exponential random graph models; Inventors; Co-inventorship networks; Knowledge flows

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.