Spatial Structure, Hierarchy and Formation Mechanisms of Scientific Collaboration Networks: Evidence of the Belt and Road Regions

Gu, Weinan; Liu, Hui

doi:10.1007/s11769-020-1161-9

Cited by 16 publications

(12 citation statements)

References 55 publications

(66 reference statements)

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“…e calculation method to represent outgoing centrality and incoming centrality is shown in equation (2), where O(m) denotes the outgoing centrality of node m, I(m) denotes incoming centrality, 􏽐N mn denotes the number of nodes pointing to other nodes, and 􏽐N nm denotes the number of nodes accepting the number of other nodes.…”

Section: Research On Service Trade Indicators Of Countries Along the "Belt And Road"mentioning

confidence: 99%

“…It will also promote the rapid development of the service industry. At this stage, the world economy is still in shock and slowly recovering, and cooperation among many regional organizations is still flourishing [2,3]. e "Belt and Road" project requires the use of multilateral cooperation mechanisms to promote the construction and sustainable development of the region.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Evolution of Service Trade Network Structure and Influence Mechanism in Countries along the “Belt and Road” with Big Data Analysis

Yang

Tsai

2022

Mathematical Problems in Engineering

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With the accelerating process of economic globalization, the deep adjustment of the international industrial division of labor, and the continuous upgrading of the global value chain, the development of the service industry has entered into a new historical period. This study analyzes the dynamic evolution of trade network structure at three levels: macro, mesa, and micro, and empirically explores its influencing factors. Taking national service trade as the research object, this study analyzes the topological structural characteristics of the food trade network of “One Belt and One Road” countries by selecting indicators such as network point degree, average path length, trade intensity, intermediation degree, and aggregation coefficient, and further explores the characteristics of association network structure in national service trade network using block model. The topological structural characteristics of the “Belt and Road” trade network are analyzed from three aspects: overall, local, and individual. Then, we construct a regression model to analyze the factors influencing the dynamic evolution of the service trade network structure of the countries along the “Belt and Road” through the QAP method. Finally, based on the summary of the research findings, this study puts forward the corresponding considerations and policy recommendations from the perspectives of regional, directional, and reciprocal investment, to benefit more efficient and strategic national service trade under the framework of “Belt and Road.” Based on the results of this study, this study proposes the following policy recommendations: firstly, actively promote the service trade cooperation with other countries to optimize the market layout; secondly, promote the service industry reform to extend the length of the value chain and improve its position in the value chain; and thirdly, jointly promote the infrastructure construction of “Belt and Road” with other countries, promote trade liberalization, and reduce the trade volume. Secondly, we should promote the reform of the service industry, extend the length of the value chain, and improve our position in the value chain. Finally, we should vigorously promote the development of innovation, promote the two-way opening of new service industries, adhere to the “going out” strategy, and cultivate high-quality and excellent technical talents, to promote the overall improvement of the innovation-driven capacity of the service industry with a people-oriented approach.

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Section: Research On Service Trade Indicators Of Countries Along the "Belt And Road"mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamic Evolution of Service Trade Network Structure and Influence Mechanism in Countries along the “Belt and Road” with Big Data Analysis

Yang

Tsai

2022

Mathematical Problems in Engineering

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“…At the end of the 20th century, the complex network theory based on mathematical graph theory and statistical physics provided a theoretical basis for studying the complexity of network systems [25][26][27]. Complex network theory and methods have been used in many fields such as disease transmission [28], transportation networks [29], scientific research cooperation networks [30], and urban networks [31,32]. is method also provides a new perspective for the identification of talent flow network structure.…”

Section: Space Of Flows Network and Talent Flowmentioning

confidence: 99%

“…where g jk is the number of shortest paths used to connect point j and point k and g jk (i) is the number of shortest paths through point i between point j and point k. [30].…”

Section: Betweenness Centralitymentioning

confidence: 99%

The Evolution of China’s High‐Level Talent Mobility Network: A Comparative Analysis Based on School and Work Stage

et al. 2022

Self Cite

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The flow of high-level talents in China is becoming more and more active and has a profound impact on the innovation and development of cities. Based on the research on the spatial distribution of the school and work stages of high-level talent in China, we study the structure and evolution of China’s high-level talent mobility network by using the data on the flow trajectory of talent among cities in China. The results show that: (1) the spatial distribution of talents in the two stages has greater consistency, but there is a change characteristic of “large dispersion, small agglomeration.” (2) Compared with the school stage, the network in the work stage has much more connections; the centrality gap of cities has narrowed. (3) “Core-periphery” structure of two networks is relatively significant and similar. The core layer in the two networks has not changed, but the subcore layer and the subedge layer have undergone changes. In addition, talents mainly flow to core and subcore cities. (4) The space structure of network is more complex in the work stage, and it is expanding to the Guangdong-Hong Kong-Macao Greater Bay Area in southeastern China.

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“…The above research has found that the high collinearity between the influence factors will have a certain impact on the GWR Model results. [63] In the results, we set the VIFs value to no more than 5 [64] for each factor. As shown in Table 5, only three meteorological factors-humidity, wind speed, and precipitation-passed the multicollinearity test.…”

Section: Analysis Of Meteorological Factorsmentioning

confidence: 99%

Spatiotemporal Distribution Characteristics and Driving Forces of PM2.5 in Three Urban Agglomerations of the Yangtze River Economic Belt

Yan

Tao

Zhang

et al. 2021

IJERPH

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As part of one of the five major national development strategies, the Yangtze River Economic Belt (YREB), including the three national-level urban agglomerations (the Cheng-Yu urban agglomeration (CY-UA), the Yangtze River Middle-Reach urban agglomeration (YRMR-UA), and the Yangtze River Delta urban agglomeration (YRD-UA)), plays an important role in China’s urban development and economic construction. However, the rapid economic growth of the past decades has caused frequent regional air pollution incidents, as indicated by high levels of fine particulate matter (PM2.5). Therefore, a driving force factor analysis based on the PM2.5 of the whole area would provide more information. This paper focuses on the three urban agglomerations in the YREB and uses exploratory data analysis and geostatistics methods to describe the spatiotemporal distribution patterns of air quality based on long-term PM2.5 series data from 2015 to 2018. First, the main driving factor of the spatial stratified heterogeneity of PM2.5 was determined through the Geodetector model, and then the influence mechanism of the factors with strong explanatory power was extrapolated using the Multiscale Geographically Weighted Regression (MGWR) models. The results showed that the number of enterprises, social public vehicles, total precipitation, wind speed, and green coverage in the built-up area had the most significant impacts on the distribution of PM2.5. The regression by MGWR was found to be more efficient than that by traditional Geographically Weighted Regression (GWR), further showing that the main factors varied significantly among the three urban agglomerations in affecting the special and temporal features.

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Spatial Structure, Hierarchy and Formation Mechanisms of Scientific Collaboration Networks: Evidence of the Belt and Road Regions

Cited by 16 publications

References 55 publications

Dynamic Evolution of Service Trade Network Structure and Influence Mechanism in Countries along the “Belt and Road” with Big Data Analysis

Dynamic Evolution of Service Trade Network Structure and Influence Mechanism in Countries along the “Belt and Road” with Big Data Analysis

The Evolution of China’s High‐Level Talent Mobility Network: A Comparative Analysis Based on School and Work Stage

Spatiotemporal Distribution Characteristics and Driving Forces of PM2.5 in Three Urban Agglomerations of the Yangtze River Economic Belt

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