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

引用 计算机科学 数据科学 背景(考古学) 互补性(分子生物学) 元数据 万维网 地理 遗传学 考古 生物
作者
Kyle Higham,Martina Contisciani,Caterina De Bacco
出处
期刊:Technological Forecasting and Social Change [Elsevier BV]
卷期号:179: 121628-121628 被引量:16
标识
DOI:10.1016/j.techfore.2022.121628
摘要

• Patent citation networks are conceptually framed as multilayer networks, wherein nodes are patent families linked across jurisdictions. • Citation contexts, such as citing office and citation type, naturally form the layers within this network. • Layers are found to be complementary rather than redundant with respect to their technological information content. • Multilayer network communities are found to contain more nuanced and technologically-relevant information than their single-layer analogues. • Extending conventional patent citation networks to analytical settings that allow more comprehensive and realistic representations of technological relationships appears to be a promising avenue of research. 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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