Exploring the network structure and nodal centrality of China

By Jiaoe Wang, Huihui Mo, Fahui Wang, Fengjun Jin. 2011

497 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/c9b8d0e6877018019d367d758ac199a3afc95144

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. L. Beauguitte
2. Cesar Ducruet

In Statistical and Nonlinear Physics

1. R. Yuan
2. L. Beauguitte
3. Jinwen Tang
4. Cesar Ducruet
5. Zhengwei Sui
6. Yu Liu
7. Hengcai Zhang
8. Teemu Makkonen

Citations in the corpus

• Uncovering Patterns of Inter-Urban Trip and Spatial Interaction from Social Media Check-In Data (2013, background): “e.g. flight networks or networks constructed from social media). With the advances in complex network research, many geographical studies introduce complex network methods into geographical analyses (Wang et al. 2011a, Wang et al. 2011b). In complex network analyses, detecting communities is an impo”
• Coupling Coordination Relationships between Urban-industrial Land Use Efficiency and Accessibility of Highway Networks: Evidence from Beijing-Tianjin-Hebei Urban Agglomeration, China (2019, methodology): “The city center function intensity index can reflect the scale of the city and its economic development level [21].”
• Exploring spatio-temporal changes of city inbound tourism flow: The case of Shanghai, China (2020, background): “…offered by the complex networks approach (see Baggio, Scott, & Cooper, 2010 for a review of network methods in the field of tourism) for the study of tourism flows have thus far mostly been restricted to related studies on transport systems (Tsiotas & Polyzos, 2015; Wang, Mo, Wang, & Jin, 2011).”
• Spatial Science and Network Science: Review and Outcomes of a Complex Relationship (2014, background): “…between a node and all the other nodes in a network) of cities in Internet and airline networks with local/urban socio-economic data such as population, and gross regional domestic product (GRDP) (Wang et al., 2011; Choi et al., 2006), thereby confirming the importance of territorial aspects.”