Opinion formation in social networks with groups: a simulation study

Abstract

This report is a study to develop a simulation model for studying the opinion formation dynamics in complex social networks with different groups. Complex network is made up of nodes and edges, similar to social networks where people are nodes and the relationship between them are edges. When interaction occurs, a relationship is formed. Complex network has many different types of networks like random network and scale-free network and each of these networks have their unique behaviours and characteristics. Random networks are formed with a disconnected set of nodes and each edge is created with a uniform probability whereas Scale-free networks are formed using a power law degree distribution. The difference between both networks is that in random networks the nodes are mostly likely to have similar number of edge connection while in Scale-free networks the number of connections of each node varies which is more useful in showing the behaviour of real-world social networks. Research has also been done on the classic deffuant model which is used to explore the formation of opinions. Before interaction, each node has its own opinion and after interacting with other nodes, their initial opinion change, forming a new opinion which may lead to making consensus. In this project, the models of the complex network will be generated and observed along with the deffuant model where multiple scenarious such as different values of threshold and different groups will be tested to observe the opinion formation.