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Bei-Bei Su
Researcher at Yangzhou University
Publications - 7
Citations - 368
Bei-Bei Su is an academic researcher from Yangzhou University. The author has contributed to research in topics: Degree distribution & Free parameter. The author has an hindex of 6, co-authored 7 publications receiving 357 citations.
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Model and empirical study on some collaboration networks
Pei-Pei Zhang,Kan Chen,Yue He,Tao Zhou,Bei-Bei Su,Ying-Di Jin,Hui Chang,Yue-Ping Zhou,Li-Cheng Sun,Bing-Hong Wang,Bing-Hong Wang,Da-Ren He,Da-Ren He +12 more
TL;DR: A simple model is suggested to show a possible evolutionary mechanism for the emergence of cooperation networks, and the analytic and numerical results obtained are in good agreement with the empirical results.
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Assortativity and act degree distribution of some collaboration networks
TL;DR: Empirical investigation results on weighted and un-weighted assortativity, act degree distribution, degree distribution and node strength distribution of nine real world collaboration networks have been presented, and one can qualitatively judge the random selection proportion of the real world network in its evolution process.
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A game theory model of urban public traffic networks
TL;DR: A simplified game theory model is proposed for simulating the evolution of the traffic network, where three network manipulators, passengers, an urban public traffic company, and a government traffic management agency play games in a network evolution process.
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Modelling collaboration networks based on nonlinear preferential attachment
Tao Zhou,Bing-Hong Wang,Ying-Di Jin,Da-Ren He,Pei-Pei Zhang,Yue He,Bei-Bei Su,Kan Chen,Zhongzhi Zhang,Jian-Guo Liu +9 more
TL;DR: The proposed model based on nonlinear preferential attachment for collaboration networks can produce the peak act-size distribution naturally that agrees with the empirical data well, and this model exhibits small-world effect, which means the corresponding networks are of very short average distance and highly large clustering coefficient.
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A general model for collaboration networks
Tao Zhou,Ying-Di Jin,Bing-Hong Wang,Da-Ren He,Pei-Pei Zhang,Yue He,Bei-Bei Su,Kan Chen,Zhongzhi Zhang +8 more
TL;DR: This model can produce the peak act-size distribution naturally that agrees with the empirical data well and exhibits small-world effect, which means the corresponding networks are of very short average distance and highly large clustering coefficient.