Please use this identifier to cite or link to this item:
|Title:||Opinion evolution in adaptive complex networks||Authors:||Xu, Jiayuan||Keywords:||Engineering::Electrical and electronic engineering||Issue Date:||2022||Publisher:||Nanyang Technological University||Source:||Xu, J. (2022). Opinion evolution in adaptive complex networks. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/160419||Abstract:||With the rapid development of information technology, information dissemination becomes more convenient, which makes public opinion events in the society happen frequently and become the focus. The report is written to study the process of opinion diversity and analyse the dynamic evolution relationship between the network structure and opinions to reveal the inherent law, which has very important practical significance. Based on the research of domestic and foreign scholars on complex networks theory and opinion dynamics, this paper uses computer simulation, theoretical modeling and analysis as methodology to reasonably abstract the interaction mode and behavior state between individuals in the network, and establishes a simulation model of opinion propagation on the complex network. The Deffaunt model is selected to conduct simulation according to the relationship between network topology structure and opinion communication. This report studies the influence of different tolerance threshold, opinion composition and rewiring probability on the evolution direction, speed and time respectively. The ”fundamental belief ” was introduced into the algorithm to make the simulated data more consistent with the reality. The experimental results show that the number of clusters when dynamic equilibrium is only related to the maximum tolerance. Finally, the report summarizes the above research and design, and evaluates the network structure change and evolution trend of network public opinion, and puts forward relevant suggestions for future research work.||URI:||https://hdl.handle.net/10356/160419||Schools:||School of Electrical and Electronic Engineering||Fulltext Permission:||restricted||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Theses|
Updated on Dec 9, 2023
Updated on Dec 9, 2023
Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.