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Finding Cross-Border Collaborative Centres in Biopharma Patent Networks: A Clustering Comparison Approach Based on Adjusted Mutual Information

Zhu, Zhen, Gao, Yuan (2022) Finding Cross-Border Collaborative Centres in Biopharma Patent Networks: A Clustering Comparison Approach Based on Adjusted Mutual Information. In: Complex Networks & Their Applications X Volume 1, Proceedings of the Tenth International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2021. Studies in Computational Intelligence , 1. pp. 62-72. Springer E-ISBN 978-3-030-93409-5. (doi:10.1007/978-3-030-93409-5_6) (KAR id:92250)

Abstract

The recent speedy development of COVID-19 mRNA vaccines has underlined the importance of cross-border patent collaboration. This paper uses the latest edition of the REGPAT database from the OECD and constructs the co-applicant patent networks for the fields of biotechnology and pharmaceuticals. We identify the cross-border collaborative regional centres in these patent networks at NUTS3 level using a clustering comparison approach based on adjusted mutual information (AMI). In particular, we measure and compare the AMI scores of the clustering before and after arbitrarily removing cross-border links of a focal node against the default clustering defined by national borders. The region with the largest difference in AMI scores is identified as the most cross-border collaborative centre, hence the name of our measure, AMI gain. We find that our measure both correlates with and has advantages over the traditional measure betweenness centrality and a simple measure of foreign share.

Item Type: Conference or workshop item (Paper)
DOI/Identification number: 10.1007/978-3-030-93409-5_6
Uncontrolled keywords: Patent networks, clustering comparison, adjusted mutual information, cross-border
Subjects: H Social Sciences
Divisions: Divisions > Kent Business School - Division > Department of Analytics, Operations and Systems
Depositing User: Zhen Zhu
Date Deposited: 09 Dec 2021 16:12 UTC
Last Modified: 05 Nov 2024 12:57 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/92250 (The current URI for this page, for reference purposes)

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