Phase Transition in the Social Impact Model of Opinion Formation in Log-Normal Networks

People may change their opinions as a consequence of interacting with others. In the literature, this phenomenon is expressed opinion formation and has wide range applications, including predicting social movements, political voting results, marketing. The interactions could be face-to-face or via online networks. phases are categorized into consensus, majority, non-majority. research, we study phase transitions due to between connected people various noise levels using agent-based modeling computational science approach. Two essential factors affect formations: model network topology. We assumed impact formation, discrete binary model, appropriate for both formation. For topology, scale-free networks have been widely used in many studies real networks, while recent revealed that most fit log-normal distributions, which considered study. Therefore, main contribution consider distribution topology results reveal two parameters transition: level segregation. A non-majority happens equilibrium high enough level, regardless majority lower levels. However, segregation, depends on affects groups’ population. comparison shows network, more segregated resistance groups against causes slightly different transition at low EI (External-Internal) index measure segregations, based difference between-group (External) links within-group (Internal) links.