Publication

Abstract : In the world of fast-growing technology, we have social media and networks have reached a place where they tend to influence the largest percentage of the population in their respective area or language. In today's world, people are influenced by popular public figures around the world. So in this research, we will identify the most influential people on social networks so that we can easily share the information, which helps in different ways like marketing, stopping the spread of false information, cautions or hazardous information, etc. This helps us spread information to large groups of people with comparatively less capital. Finding influential people can now be done by finding influential nodes in social networks. Different researchers around the world have proposed various ways of finding influential nodes like PageRank, degree centrality, betweenness centrality, closeness centrality, etc. Of these, some come under global-structure-based and some come under local-structure-based. Our idea is for an independent cascade model to be applied to the basic centralities to test the spreading ability. We analyze the relationship between centrality values and information spread. Finally, in this research, we will discuss various centralities that help in finding influential nodes and pick the best centrality depending on the cause or situation.