欧洲地区的三角研究和创新合作
Triangular research and innovation collaborations in the European area
摘要：
在当前的研究中，我们研究了专利和欧洲框架计划（FP）的多重网络，旨在揭示三角形（任何三个节点之间的完全连接的网络）形成模式的时间变化。更具体地，多路复用网络由两层组成，其节点是NUTS 2区域。在第一层，我们描绘了为创造专利而合作的发明者的区域，在第二层，我们描绘了欧洲框架计划（FP）资助项目中的科学家的区域。当来自不同地区的科学家或发明家合作时，两个节点之间就存在着联系。我们将网络在时间上分成28个较短的子网络，每个子网络的跨度为6年，并计算在6年结束时形成的三角形的数量。接下来，我们重新梳理数据，创建28个六年随机网络，以确定是否存在有利于非随机行为的隐藏机制。真实数据和混洗数据使用z分数进行比较，z分数是它们之间标准差差异的度量。此外，我们使用聚类系数重复相同的分析，聚类系数是三角形的数量除以三元组（可能的三角形）的数量。结果表明，三角形FP合作往往比随机的，而在专利的情况下，情况正好相反。此外，使用三角形的结果往往是更全面的，而不是那些聚类系数。最后，我们确定了NUTS2区域在任一层中经常表现出高聚类系数，并为所有区域提供了一个具有这些值的地图。本研究的结果可以帮助政策制定机构了解补贴研究和专利创新合作网络的空间维度。
英文摘要：
In the current study, we examine the multiplex network of patents and European Framework Programmes (FPs) aiming to uncover temporal variations in the formation patterns of triangles (a fully connected network between any three nodes). More specifically, the multiplex network consists of two layers whose nodes are the NUTS2 regions. On the first layer we depict the regions of the inventors that collaborated for the creation of a patent, and on the second those of the scientists in European Framework Programme (FP) funded projects. A link between two nodes exists when scientists or inventors from different regions collaborate. We split the network temporally into 28 shorter sub-networks with a span of 6 years each, and calculate the number of triangles formed at the end of the 6-year period. Next, we shuffle the data creating again 28 six-year randomized networks, in order to identify whether there is a hidden mechanism that favors a non-random behavior. Real and shuffled data are compared using a z-score, a measure of the differences of standard deviations between them. In addition, we repeat the same analysis using the clustering coefficient, which is the number of triangles over the number of triples (possible triangles). The results show that triangular FP collaborations tend to be favored over random ones, while in patents the case is strongly the opposite. Furthermore, results using triangles tend to be more comprehensive as opposed to those of the clustering coefficient. Finally, we identify which NUTS2 regions frequently exhibit a high clustering coefficient in either of the layers, and we present a map with these values for all regions. The results of this research can help policy making organizations understand the spatial dimension of subsidized research and patented innovation collaboration networks.
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。
