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Contribution Details

Type Journal Article
Scope Discipline-based scholarship
Title Early identification of important patents: Design and validation of citation network metrics
Organization Unit
  • Manuel Sebastian Mariani
  • Matúš Medo
  • François Lafond
Item Subtype Original Work
Refereed Yes
Status Published in final form
  • English
Journal Title Technological Forecasting and Social Change
Publisher Elsevier
Geographical Reach international
ISSN 0040-1625
Volume 146
Page Range 644 - 654
Date 2019
Abstract Text One of the most challenging problems in technological forecasting is to identify as early as possible those technologies that have the potential to lead to radical changes in our society. In this paper, we use the US patent citation network (1926–2010) to test our ability to early identify a list of expert-selected historically significant patents through citation network analysis. We show that in order to effectively uncover these patents shortly after they are issued, we need to go beyond raw citation counts and take into account both the citation network topology and temporal information. In particular, an age-normalized measure of patent centrality, called rescaled PageRank, allows us to identify the significant patents earlier than citation count and PageRank score. In addition, we find that while high-impact patents tend to rely on other high-impact patents in a similar way as scientific papers, the patents' citation dynamics is significantly slower than that of papers, which makes the early identification of significant patents more challenging than that of significant papers. In the context of technology management, our rescaled metrics can be useful to early detect recent trends in technical improvement, which is of fundamental interest for companies and investors.
Digital Object Identifier 10.1016/j.techfore.2018.01.036
Other Identification Number merlin-id:16863
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