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

Zhu, Zhen; Gao, Yuan

doi:10.1007/978-3-030-93409-5_6

Cited by 4 publications

(6 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this paper, we propose using clustering comparison in another application: as a way of identifying central nodes in networks. In the previous analysis (Zhu and Gao 2021), we have found that our proposed measure both correlates with and has advantages over the traditional measure of betweenness centrality as it better differentiates cross-border centres from local ones and offers a more uniform distribution of values. Our work also shows that compared to a simple measure of foreign share, AMI gain is more of a global and structural measure and better differentiates the nodes on the top.…”

mentioning

confidence: 79%

See 1 more Smart Citation

Regional industrial growth and biopharma patent networks: empirical insights from the UK

Gao

Zhu

2022

Appl Netw Sci

Self Cite

View full text Add to dashboard Cite

The COVID-19 pandemic has once again brought the significance of biopharmaceutical and medical technology sectors to the spotlight. Seeing that some of the most critical medical breakthroughs such as the speedy mRNA vaccine development were results of cross-border patenting collaboration, we have proposed in a previous work a new method to identify the cross-border collaborative regional centres in the patent networks, using a clustering comparison approach based on adjusted mutual information (AMI). In this paper, we focus on the UK industrial landscape. We use the UK bioscience and health technology sector statistics from 2015 to 2020 and look into the regional growth of each postcode area. We compare the top growth regions with the cross-border collaborative centres identified using AMI comparison at the postcode area level, and find that both long-term and short-term AMI gains show an increase in the correlation with regional annual growth rates of firm numbers in the studied sectors from 2016 to 2020, and the increase is more consistent with the short-term AMI gain. We also found that areas more central in the long-term cross-regional R&D collaboration demonstrate a stronger association with more developed industrial settings indicated by more firms and, potentially more employment and turnover in the field. However, AMI gains are found to have negative correlations with the industrial growths as a sign of possible trade-offs of being central.

show abstract

mentioning

confidence: 79%

“…The development of the AMI gain method is detailed in our previous work (Zhu and Gao 2021). We provide a brief review here: For the constructed network with weighted links, we restrict our focus to the largest components and use the Louvain method (Blondel et al 2008) for community detection.…”

Section: Ami Gain Algorithmmentioning

confidence: 99%

Regional industrial growth and biopharma patent networks: empirical insights from the UK

Gao

Zhu

2022

Appl Netw Sci

Self Cite

View full text Add to dashboard Cite

show abstract

“…The development of the AMI gain method is detailed in out previous work [25] and has been adapted for this study: First, the original method is based on NUTS3 level region division, while in this paper it's been revised to map to the UK postcode areas. Second, in this study we combine pharmaceuticals and biotechnology patents together.…”

Section: Ami Gain Algorithmmentioning

confidence: 99%

“…The definiton of AMI is the same as in the previous work [25]. Algorithm 1 shows the adapted pseudocode of calculating the AMI gain for each node.…”

Section: Ami Gain Algorithmmentioning

confidence: 99%

Regional Industrial Growth and Biopharma Patent Networks Empirical Insights from the UK

Gao

Zhang

2022

Preprint

Self Cite

View full text Add to dashboard Cite

The COVID-19 pandemic has once again brought the significance of biopharmaceutical and medical technology sectors to the spotlight. Seeing that some of the most critical medical breakthroughs such as the speedy mRNA vaccine development were results of cross-border patenting collaboration, we have proposed in a previous work a new method to identify the cross-border collaborative regional centres in the patent networks, using on a clustering comparison approach based on adjusted mutual information (AMI). In this paper, we focus on the UK industrial landscape. We use the UK bioscience and health technology sector statistics from 2015 to 2020 and look into the regional growth of each postcode area. We compare the top growth regions with the cross-border collaborative centers identified using AMI comparison at the postcode area level, and find that areas more central in the long-term cross-regional R&D collaboration tend to have more developed industrial settings with higher business numbers and, potentially more employment and turnover in the field.

show abstract

“…When using patent data, regional studies investigated biotechnology cooperation in Brazil ( 22 ), tuberculosis prevention and treatment in Brazil ( 23 ), collaborative innovation in Chinese medicine ( 24 ), the impact of patent cooperation on firm patent output in the pharmaceutical industry in Beijing-Tianjin-Hebei region in China ( 25 ), and the impact of patent cooperation network on small and medium-sized enterprises in the pharmaceutical industry of China 1 , etc., thereby providing guidance for decision-making on regional public health development. On a global scale, Liu et al ( 26 ) investigated the global landscape of patents related to human coronaviruses; and Zhu and Gao ( 27 ) analyzed the global biopharmaceutical patent cooperation network using a clustering comparison approach, and identified cross-border regional collaborative centers.…”

Section: Introductionmentioning

confidence: 99%

Structural characteristics of China's COVID-19 patent cooperation network at the province, city, and patent applicant levels

Xia

Wang

Zhang

et al. 2022

Front. Public Health

View full text Add to dashboard Cite

The Chinese Plan has provided an important model for the global fight against COVID-19 since its outbreak. The present study describes the structural characteristics of China's COVID-19 patent cooperation network at the province, city, and applicant levels by using social network analysis based on data from the Incopat global patent database since 2020, which helps to clarify the current technology accumulation in this field in China, and provide patent information support for the scientific efforts to fight against COVID-19. The findings are as follows: First, the inter-regional cooperation level in China's COVID-19 patent cooperation network shows a decreasing trend from eastern to central to western regions. At the inter-applicant cooperation level, kinship-based cooperation is the strongest, business-based cooperation has the widest scope, while proximity-based cooperation exists throughout these two main models of cooperation. Second, coastal provinces and cities occupy a core position in the network, and play an important role in utilizing structural holes and bridging. Patent applicants with high centrality are mostly firms. Research institutes and universities mainly play the role of bridges. Third and lastly, there is no large number of cliques at the province and city levels. However, there is a tendency for cliques to develop at the applicant level. Hence, actions are needed to prevent the development of information barriers.

show abstract

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

Cited by 4 publications

References 20 publications

Regional industrial growth and biopharma patent networks: empirical insights from the UK

Regional industrial growth and biopharma patent networks: empirical insights from the UK

Regional Industrial Growth and Biopharma Patent Networks Empirical Insights from the UK

Structural characteristics of China's COVID-19 patent cooperation network at the province, city, and patent applicant levels

Contact Info

Product

Resources

About