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

Fritz, Cornelius; De Nicola, Giacomo; Kevork, Sevag; Harhoff, Dietmar; Kauermann, Göran

doi:10.1093/jrsssa/qnad009

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

MPG-Autoren

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Harhoff, Dietmar
MPI for Innovation and Competition, Max Planck Society;

Externe Ressourcen

https://doi.org/10.48550/arXiv.2201.09744
(Preprint)

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Zitation

Fritz, C., De Nicola, G., Kevork, S., Harhoff, D., & Kauermann, G. (2023). Modelling the Large and Dynamically Growing Bipartite Network of German Patents and Inventors. Journal of the Royal Statistical Society, Series A: Statistics in Society, 186(3), 557-576. doi:10.1093/jrsssa/qnad009.

Zitierlink: https://hdl.handle.net/21.11116/0000-000C-826D-7

Zusammenfassung

To explore the driving forces behind innovation, we analyse the dynamic bipartite network of all inventors and patents registered within the field of electrical engineering in Germany in the past two decades. To deal with the sheer size of the data, we decompose the network by exploiting the fact that most inventors tend to only stay active for a relatively short period. We thus propose a Temporal Exponential Random Graph Model with time-varying actor set and sufficient statistics mirroring substantial expectations for our analysis. Our results corroborate that inventor characteristics and team formation are essential to the dynamics of invention.