Co-evolution of opinions and social network: a simulation study

Studying social network graphs has always been a fascinating topic since we can learn more about real-life human interactions with one another using social interaction simulations paired with varying parameters in different scenarios. Although there have been prior studies conducted about consens...

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Main Author: Low, John Enze
Other Authors: Xiao Gaoxi
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2022
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Online Access:https://hdl.handle.net/10356/157395
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1573952023-07-07T19:11:55Z Co-evolution of opinions and social network: a simulation study Low, John Enze Xiao Gaoxi School of Electrical and Electronic Engineering EGXXiao@ntu.edu.sg Engineering::Electrical and electronic engineering::Computer hardware, software and systems Studying social network graphs has always been a fascinating topic since we can learn more about real-life human interactions with one another using social interaction simulations paired with varying parameters in different scenarios. Although there have been prior studies conducted about consensus making between nodes in a social network graph, little is known about the repulsive effect when 2 nodes disagree with each other. Therefore, the goal of this research is to examine the reactions of nodes if they are in a situation of disagreement with their neighbors and observe how the overall social network structure is like after the social network reaches a steady state. This study was carried out in Jupyter acting as an IDE, with Python as the main language of use. 1 basic model and 5 other situational models were used in this study. The steps of the study are relatively straightforward. A virtual environment is first created for the social network graph, followed by the actual creation of the graph itself, and finally the implementation of different parameters onto the individual nodes. The results of the situational models where repulsive effects are present without disruption were all similar when pushed to its steady states which points to all its nodes having values of 0 or 1 only. However, the observations between all these models before its steady state is reached is slightly different. In scenarios where the repulsive effect is met with resistance such as nodes being left alone after a certain threshold, the steady state values consist of 3 distinct communities with their opinion values at 0, 0.5, and 1. Although 5 different scenarios were implemented in replicating a real-life scenario, it is evident that many more parameters must be introduced to these models in order to achieve a higher accuracy of real-life opinion repulsive depiction. Bachelor of Engineering (Electrical and Electronic Engineering) 2022-05-14T12:23:41Z 2022-05-14T12:23:41Z 2022 Final Year Project (FYP) Low, J. E. (2022). Co-evolution of opinions and social network: a simulation study. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/157395 https://hdl.handle.net/10356/157395 en A3289-211 application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering::Computer hardware, software and systems
spellingShingle Engineering::Electrical and electronic engineering::Computer hardware, software and systems
Low, John Enze
Co-evolution of opinions and social network: a simulation study
description Studying social network graphs has always been a fascinating topic since we can learn more about real-life human interactions with one another using social interaction simulations paired with varying parameters in different scenarios. Although there have been prior studies conducted about consensus making between nodes in a social network graph, little is known about the repulsive effect when 2 nodes disagree with each other. Therefore, the goal of this research is to examine the reactions of nodes if they are in a situation of disagreement with their neighbors and observe how the overall social network structure is like after the social network reaches a steady state. This study was carried out in Jupyter acting as an IDE, with Python as the main language of use. 1 basic model and 5 other situational models were used in this study. The steps of the study are relatively straightforward. A virtual environment is first created for the social network graph, followed by the actual creation of the graph itself, and finally the implementation of different parameters onto the individual nodes. The results of the situational models where repulsive effects are present without disruption were all similar when pushed to its steady states which points to all its nodes having values of 0 or 1 only. However, the observations between all these models before its steady state is reached is slightly different. In scenarios where the repulsive effect is met with resistance such as nodes being left alone after a certain threshold, the steady state values consist of 3 distinct communities with their opinion values at 0, 0.5, and 1. Although 5 different scenarios were implemented in replicating a real-life scenario, it is evident that many more parameters must be introduced to these models in order to achieve a higher accuracy of real-life opinion repulsive depiction.
author2 Xiao Gaoxi
author_facet Xiao Gaoxi
Low, John Enze
format Final Year Project
author Low, John Enze
author_sort Low, John Enze
title Co-evolution of opinions and social network: a simulation study
title_short Co-evolution of opinions and social network: a simulation study
title_full Co-evolution of opinions and social network: a simulation study
title_fullStr Co-evolution of opinions and social network: a simulation study
title_full_unstemmed Co-evolution of opinions and social network: a simulation study
title_sort co-evolution of opinions and social network: a simulation study
publisher Nanyang Technological University
publishDate 2022
url https://hdl.handle.net/10356/157395
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