为德国专利和发明人的大型动态增长双向网络建模
Modelling the large and dynamically growing bipartite network of German patents and inventors
摘要：
我们分析了德国电气工程主要领域内注册的发明人和专利的双向动态网络，以探索创新背后的驱动力。手头的数据导致了一个二分网络，如果发明者是相应专利的共同所有人，则发明者和专利之间存在边缘。由于在观察期间，发明者申请了10万多项专利，这相当于一个庞大的二分网络，太大了，无法作为一个整体进行分析。因此，我们通过利用网络的一个基本特征来分解二分网络：大多数发明者倾向于只在相对较短的时间内保持活跃，而新的发明者在每个时间点都变得活跃。因此，邻接矩阵携带多个结构零。为了适应这些，我们提出了时间指数随机图模型（TERGM）的二分变体，在该模型中，我们让演员集随时间变化，区分已经提交专利的发明人和那些没有提交专利的发明人，并考虑发明人的成对统计数据。我们的研究结果证实了假设，发明家的特点和知识流在发明的动力学中起着至关重要的作用。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
