Examining the evolving structures of intercity knowledge networks: the case of scientific collaboration in China

ABSTRACT

Drawing on data on scientific co-publications derived from the Web of Science for the periods 2002–2006 and 2012–2016, we construct and analyse a key element of China's intercity knowledge networks (CIKNs): scientific collaboration networks. Employing network-analytical and exponential random graph modelling techniques, we examine the evolving structures and driving mechanisms underlying these CIKNs. Our results show that the density of the CIKNs has significantly increased over time. CIKN flows are dense in the Southeastern but sparse in the Northwestern part of China, with the Hu Line acting as a clearly visible border. As the dominant knowledge centre, Beijing is involved in scientific collaboration networks throughout the country, with the diamond-shaped structure anchored by Beijing-Shanghai-Guangzhou-Chengdu becoming evident. We find that preferential attachment and transitivity are significant endogenous processes driving scientific collaboration, while a city's administrative level and R&D investment are the strongest exogenous factors. The impact of GDP and geographical proximity is limited, with institutional proximity being the most sizable of the well-known suite of proximity effects.

KEYWORDS:

• Intercity knowledge network
• scientific collaboration
• social network analysis
• exponential random graph model
• China

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 Data source: https://ncses.nsf.gov/pubs/nsb20206/publication-output-by-region-country-or-economy

Additional information

Funding

The work is supported by the National Natural Science Foundation of China (41901189, 52008298), Natural Science Foundation of Jiangsu Province (BK20190797), China Postdoctoral Science Foundation (2020M671229), Natural Science Foundation of the Higher Education Institutions of Jiangsu Province (19KJB170016) and KU Leuven C1 Project (C14/21/021).