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Weighted Node Importance Contribution Correlation Matrix for Identifying China’s Core Metro Technologies with Patent Network Analysis

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Knowledge Science, Engineering and Management (KSEM 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9983))

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Abstract

The purpose of this study is to identify the core technologies in the metro domain by analyzing its patent network, which is beneficial for grasping technological trends and advancing the metro domain in China. Metro patent data (1986–2016) published in China were collected from the State Intellectual Property Office of the People’s Republic of China. Then, we built a patent network with co-occurrence of information from the International Patent Classification, and improved the node importance contribution correlation matrix method to a weighted version in order to calculate the importance of each node. Nodes with high importance scores play more crucial roles in efficiency and stability of the network, and are viewed as the core metro technologies. The results can be useful for companies’ technology R&D planning and government policymaking.

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Acknowledgments

This research was sponsored by NSFC (No.71125002).

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Authors and Affiliations

  1. Business School, East China University of Science and Technology, Shanghai, China

    Mei Long & Tieju Ma

Authors
  1. Mei Long
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  2. Tieju Ma
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Corresponding author

Correspondence to Mei Long .

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Editors and Affiliations

  1. University of Passau, Passau, Germany

    Franz Lehner

  2. University of Passau , Passau, Germany

    Nora Fteimi

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Long, M., Ma, T. (2016). Weighted Node Importance Contribution Correlation Matrix for Identifying China’s Core Metro Technologies with Patent Network Analysis. In: Lehner, F., Fteimi, N. (eds) Knowledge Science, Engineering and Management. KSEM 2016. Lecture Notes in Computer Science(), vol 9983. Springer, Cham. https://doi.org/10.1007/978-3-319-47650-6_16

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  • DOI: https://doi.org/10.1007/978-3-319-47650-6_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47649-0

  • Online ISBN: 978-3-319-47650-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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