Performance Analysis of Opinion Dynamics Models Over Time Varying Networks

Abstract

People share information and exchange opinions in their day-to-day lives through various newlineoffline and online interactions. For example, they may discuss related to a product newlineto make an opinion about it. Spreading of fake news related to any issue or a product on newlinesocial platforms can affect the opinions of the people [1]. Politicians can affect the opinion newlinevalues of the voters by their manifestos to affect the casting of the votes. People are connected newlinethrough their social contacts to construct a social network. Complex network is the newlinebackbone of the social network where nodes of the network represent the people, and the newlineedges account for their interactions. Many researchers use static networks to study opinion newlinedynamics [2,3]. However, this is far from the real- world scenario, where opinions, interactions, newlineand population continuously evolve as per time. Nodes and edges in the network may newlineappear as well as disappear at different time instances. A constantly evolving network is newlinetermed as Time Varying or Temporal Network. Information flows over the network creates newlineopinions among the people. Opinions of people keep on affecting their neighbour s opinions. newlineThis is how opinion dynamics work. Dynamicity of both the opinions and network newlinestructure is achieved based on various properties of the network. newlineIntend of the present work documented in this thesis is to design some time-varying newlineopinion dynamics network models. Opinion values are shared by the nodes from their newlineneighbours in the proposed Time Varying Network models. It is analyzed how the opinion newlinevalues flow among the network and reaches the convergence state.