Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/151606
Title: Opinion formation in growing social systems
Authors: Zhang, Shibo
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2021
Publisher: Nanyang Technological University
Source: Zhang, S. (2021). Opinion formation in growing social systems. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/151606
Abstract: Many complex systems in social sciences can be described by complex networks. Different from regular networks and random networks, real complex networks have scale-free characteristics. Some well-known network growth models and some opinion evolution models only consider the following cases on social networks: the opinion evolution has no influence on the network growth. But in the real world, opinion formation may play an important role in system growth, while people may tend to be connected to similar opinion holders and be well accepted by similar opinion holders. Motivated by such observations, we focused on the interaction between the topology of complex networks and the opinion evolution in this paper. Our main objective is to study how the topology changes when opinion plays a role in network growth, and then to analyze whether the power law is truly universal. First of all, we analyzed the case that the evolution of opinions takes place after the network grows, and then we focused on the simultaneous occurrence of network growth and opinion evolution, and verified our conclusion by changing three different conditions. We found that when the evolution of opinion has no effect on the growth of the system, the network topology is consistent with the original BA scale-free network topology at the end of iteration. While the evolution of opinion plays an important role in the growth of the system, due to the interaction between the evolution of opinions and the network topology, the network seems to conform more to a lognormal distribution than to the original power law distribution.
URI: https://hdl.handle.net/10356/151606
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

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