Ranking Popular Personalities in Social Networks Using Mixed Centrality Method

In today's world, social networks play a crucial role by providing individuals with a platform to engage, exchange knowledge, and exert influence on others. Finding popular personalities in these networks is important for several purposes, including marketing, information sharing, and opinion forming. To determine influential individuals in social networks, traditional centrality metrics such as degree centrality and betweenness centrality have been extensively used. On the other hand, these measurements usually concentrate on the local or global topological structure both inside and outside of networks. Specifically, a lot of measures restrict their capacity to record the total impact of the node on small-scale networks. To address these challenges, we design a new measure called mixed centrality (MC2) which are focus on the union of the local and global formation of the network. To shown the effectiveness of the proposed measure, we compare the our measures with Degree, Betweenness, Closeness, Cluster-Coefficient, Local and Global centrality measures. We employ the SIR (Susceptible-Infected-Recovered) model to investigate the extreme data dissemination of our centrality metrics compared to traditional measures and to pull off in-depth simulations on big concrete data sets such as jb-pages-company, facebook-combined, jb-pages-public-figure and .fb-pages-government.