Recently, random graphs in which vertices are characterized by hidden variables controlling the establishment of edges between pairs of vertices have attracted much attention. This paper presents a specific realizatio...Recently, random graphs in which vertices are characterized by hidden variables controlling the establishment of edges between pairs of vertices have attracted much attention. This paper presents a specific realization of a class of random network models in which the connection probability between two vertices （i, j） is a specific function of degrees ki and kj. In the framework of the configuration model of random graphsp we find the analytical expressions for the degree correlation and clustering as a function of the variance of the desired degree distribution. The obtained expressions are checked by means of numerical simulations. Possible applications of our model are discussed.展开更多
The global clustering of inventive talent shapes innovation capacity and drives economic growth.For China,this process is especially crucial in sustaining its development momentum.This paper draws on data from the EPO...The global clustering of inventive talent shapes innovation capacity and drives economic growth.For China,this process is especially crucial in sustaining its development momentum.This paper draws on data from the EPO Worldwide Patent Statistical Database(PATSTAT)to extract global inventive talent mobility information and analyzes the spatial structural evolution of the global inventive talent flow network.The study finds that this network is undergoing a multi-polar transformation,characterized by the rising importance of a few central countries-such as the United States,Germany,and China-and the increasing marginalization of many peripheral countries.In response to this typical phenomenon,the paper constructs an endogenous migration model and conducts empirical testing using the Temporal Exponential Random Graph Model(TERGM).The results reveal several endogenous mechanisms driving global inventive talent flows,including reciprocity,path dependence,convergence effects,transitivity,and cyclic structures,all of which contribute to the network’s multi-polar trend.In addition,differences in regional industrial structures significantly influence talent mobility choices and are a decisive factor in the formation of poles within the multi-polar landscape.Based on these findings,it is suggested that efforts be made to foster two-way channels for talent exchange between China and other global innovation hubs,in order to enhance international collaboration and knowledge flow.We should aim to reduce the migration costs and institutional barriers faced by R&D personnel,thereby encouraging greater mobility of high-skilled talent.Furthermore,the government is advised to strategically leverage regional strengths in high-tech industries as a lever to capture competitive advantages in emerging technologies and products,ultimately strengthening the country’s position in the global innovation landscape.展开更多
The Internet of Things(IoT)has the potential to be applied to social networks due to innovative characteristics and sophisticated solutions that challenge traditional uses.Social network analysis(SNA)is a good example...The Internet of Things(IoT)has the potential to be applied to social networks due to innovative characteristics and sophisticated solutions that challenge traditional uses.Social network analysis(SNA)is a good example that has recently gained a lot of scientific attention.It has its roots in social and economic research,as well as the evaluation of network science,such as graph theory.Scientists in this area have subverted predefined theories,offering revolutionary ones regarding interconnected networks,and they have highlighted the mystery of six degrees of separation with confirmation of the small-world phenomenon.The motivation of this study is to understand and capture the clustering properties of large networks and social networks.We present a network growth model in this paper and build a scale-free artificial social network with controllable clustering coefficients.The random walk technique is paired with a triangle generating scheme in our proposed model.As a result,the clustering controlmechanism and preferential attachment(PA)have been realized.This research builds on the present random walk model.We took numerous measurements for validation,including degree behavior and the measure of clustering decay in terms of node degree,among other things.Finally,we conclude that our suggested random walk model is more efficient and accurate than previous state-of-the-art methods,and hence it could be a viable alternative for societal evolution.展开更多
基金Project supported by the National Natural Science Foundation of China （Grant Nos 10375025 and 10275027） and the Cultivation Fund of the Key Scientific and Technical Innovation Project, Ministry of Education of China （Grant No 704035）
文摘Recently, random graphs in which vertices are characterized by hidden variables controlling the establishment of edges between pairs of vertices have attracted much attention. This paper presents a specific realization of a class of random network models in which the connection probability between two vertices （i, j） is a specific function of degrees ki and kj. In the framework of the configuration model of random graphsp we find the analytical expressions for the degree correlation and clustering as a function of the variance of the desired degree distribution. The obtained expressions are checked by means of numerical simulations. Possible applications of our model are discussed.
基金supported by the Major Project of the National Social Science Fund of China,titled“Design Path Selection for the Mechanism of New and Old Growth Driver Conversion”(Grant No.18ZDA077)by the Joint Special Major Research Project of the Yangtze River Delta Economics and Social Development Research Center at Nanjing University and the Collaborative Innovation Center for China Economy(CICCE),titled“Practicing Innovation in China’s Development Economics for the Yangtze River Delta:From Industrial Clusters to Technological Clusters”(Grant No.CYD2022006).
文摘The global clustering of inventive talent shapes innovation capacity and drives economic growth.For China,this process is especially crucial in sustaining its development momentum.This paper draws on data from the EPO Worldwide Patent Statistical Database(PATSTAT)to extract global inventive talent mobility information and analyzes the spatial structural evolution of the global inventive talent flow network.The study finds that this network is undergoing a multi-polar transformation,characterized by the rising importance of a few central countries-such as the United States,Germany,and China-and the increasing marginalization of many peripheral countries.In response to this typical phenomenon,the paper constructs an endogenous migration model and conducts empirical testing using the Temporal Exponential Random Graph Model(TERGM).The results reveal several endogenous mechanisms driving global inventive talent flows,including reciprocity,path dependence,convergence effects,transitivity,and cyclic structures,all of which contribute to the network’s multi-polar trend.In addition,differences in regional industrial structures significantly influence talent mobility choices and are a decisive factor in the formation of poles within the multi-polar landscape.Based on these findings,it is suggested that efforts be made to foster two-way channels for talent exchange between China and other global innovation hubs,in order to enhance international collaboration and knowledge flow.We should aim to reduce the migration costs and institutional barriers faced by R&D personnel,thereby encouraging greater mobility of high-skilled talent.Furthermore,the government is advised to strategically leverage regional strengths in high-tech industries as a lever to capture competitive advantages in emerging technologies and products,ultimately strengthening the country’s position in the global innovation landscape.
基金This work was supported in part by the Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education under Grant NRF-2019R1A2C1006159 and Grant NRF-2021R1A6A1A03039493in part by the 2021 Yeungnam University Research Grant。
文摘The Internet of Things(IoT)has the potential to be applied to social networks due to innovative characteristics and sophisticated solutions that challenge traditional uses.Social network analysis(SNA)is a good example that has recently gained a lot of scientific attention.It has its roots in social and economic research,as well as the evaluation of network science,such as graph theory.Scientists in this area have subverted predefined theories,offering revolutionary ones regarding interconnected networks,and they have highlighted the mystery of six degrees of separation with confirmation of the small-world phenomenon.The motivation of this study is to understand and capture the clustering properties of large networks and social networks.We present a network growth model in this paper and build a scale-free artificial social network with controllable clustering coefficients.The random walk technique is paired with a triangle generating scheme in our proposed model.As a result,the clustering controlmechanism and preferential attachment(PA)have been realized.This research builds on the present random walk model.We took numerous measurements for validation,including degree behavior and the measure of clustering decay in terms of node degree,among other things.Finally,we conclude that our suggested random walk model is more efficient and accurate than previous state-of-the-art methods,and hence it could be a viable alternative for societal evolution.
