论文标题

多层专利引文网络:研究明确的技术关系的全面分析框架

Multilayer patent citation networks: A comprehensive analytical framework for studying explicit technological relationships

论文作者

Higham, Kyle, Contisciani, Martina, De Bacco, Caterina

论文摘要

在创新研究领域,使用专利引文网络作为研究工具变得越来越普遍。但是,这些网络很少考虑产生这些引用的上下文,并且通常仅限于单个管辖权。在这里,我们建议并探索使用多层网络框架的使用,该框架自然可以包含引用元数据并跨司法管辖区延伸,从而可以完整地了解通过专利数据访问的全球技术景观。采用一种将引文网络层通过三元专利家族联系起来的保守方法,我们首先观察到这些层包含有关技术关系的互补而不是多余的信息。为了探究这种互补性的性质,我们从多层网络和类似的单层网络中提取网络社区,然后直接将其技术组成与既定的技术相似性网络进行比较。我们发现,虽然技术在多层案例中的社区中更加分裂,但提取的社区匹配已建立的网络。我们得出的结论是,通过捕获引文上下文,在概念和经验上,专利引用网络的多层表示可以更好地捕获与传统的单层方法相比,在实际技术关系中存在着重要的细微差别。我们建议未来的研究途径,利用旨在与多层网络一起使用的新型计算工具。

The use of patent citation networks as research tools is becoming increasingly commonplace in the field of innovation studies. However, these networks rarely consider the contexts in which these citations are generated and are generally restricted to a single jurisdiction. Here, we propose and explore the use of a multilayer network framework that can naturally incorporate citation metadata and stretch across jurisdictions, allowing for a complete view of the global technological landscape that is accessible through patent data. Taking a conservative approach that links citation network layers through triadic patent families, we first observe that these layers contain complementary, rather than redundant, information about technological relationships. To probe the nature of this complementarity, we extract network communities from both the multilayer network and analogous single-layer networks, then directly compare their technological composition with established technological similarity networks. We find that while technologies are more splintered across communities in the multilayer case, the extracted communities match much more closely the established networks. We conclude that by capturing citation context, a multilayer representation of patent citation networks is, conceptually and empirically, better able to capture the significant nuance that exists in real technological relationships when compared to traditional, single-layer approaches. We suggest future avenues of research that take advantage of novel computational tools designed for use with multilayer networks.

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