  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.
