AN EVOLUTIONARY MODEL OF SOCIAL NETWORK STRUCTURE DRIVEN BY INFORMATION INTERACTION

Fuzhong Nian, Jianjian Zhou () and Yinuo Qian ()
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Fuzhong Nian: School of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, P. R. China
Jianjian Zhou: School of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, P. R. China
Yinuo Qian: School of Control Science and Engineering, Tiangong University, Tianjin 300387, P. R. China

Advances in Complex Systems (ACS), 2023, vol. 26, issue 04n05, 1-23

Abstract: The interaction of information and the evolution of network structure are inseparable. In order to construct social network evolution and information propagation models that better fit real-world scenarios, this paper proposes a social network structure evolution model driven by changes in the strength of relationships between individuals through their information interactions with each other. During the evolution process of the network, information interaction between individuals is also influenced by the network structure. Therefore, we improve traditional propagation models and construct an information propagation model with dynamic propagation rates. The proposed model is used to simulate both the spread of information and the evolution of network structures in real social networks. Finally, simulation results are compared to real-world data, demonstrating the effectiveness and rationality of the proposed model.

Keywords: Network evolution; information propagation; weighted network; dynamic propagation rate (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1142/S0219525923500108

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