Skip navigation

Consistency and trends of technological innovations: A network approach to the international patent classification data

Consistency and trends of technological innovations: A network approach to the international patent classification data

Gao, Yuan, Zhu, Zhen ORCID: 0000-0003-0258-1454 and Riccaboni, Massimo (2017) Consistency and trends of technological innovations: A network approach to the international patent classification data. In: Complex Networks & Their Applications VI. Studies in Computational Intelligence, 689 . Springer, Cham, pp. 744-756. ISBN 978-3-319-72149-1 ISSN 1860-949X (doi:https://doi.org/10.1007/978-3-319-72150-7_60)

Full text not available from this repository.

Abstract

Classifying patents by the technology areas they pertain is important to enable information search and facilitate policy analysis and socio-economic studies. Based on the OECD Triadic Patent Family database, this study constructs a cohort network based on the grouping of IPC subclasses in the same patent families, and a citation network based on citations between subclasses of patent families citing each other. This paper presents a systematic analysis approach which obtains naturally formed network clusters identified using a Lumped Markov Chain method, extracts community keys traceable over time, and investigates two important community characteristics: consistency and changing trends. The results are verified against several other methods, including a recent research measuring patent text similarity. The proposed method contributes to the literature a network-based approach to study the endogenous community properties of an exogenously devised classification system. The application of this method may improve accuracy and efficiency of the IPC search platform and help detect the emergence of new technologies.

Item Type: Conference Proceedings
Title of Proceedings: Complex Networks & Their Applications VI
Additional Information: International Conference on Complex Networks and their Applications
Uncontrolled Keywords: patents; innovation; networks; community detection
Faculty / Department / Research Group: Faculty of Business
Faculty of Business > Centre for Business Network Analysis (CBNA)
Faculty of Business > Networks and Urban Systems Centre (NUSC) > Centre for Business Network Analysis (CBNA)
Faculty of Business > Department of International Business & Economics
Last Modified: 16 Apr 2018 10:37
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/18558

Actions (login required)

View Item View Item