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The authors consider the following advertisement problem in Online Social Networks (OSNs). Given a fixed advertisement investment, e.g., a number of free samples that can be given away to a small number of users, a company needs to determine the probability that users in the OSN will eventually purchase the product. In this paper, they model OSNs as scale-free graphs (either with or without high clustering coefficient). They employ various influence mechanisms that govern the influence spreading in such large scale OSNs and use the Local Mean Field (LMF) technique to analyze these online social networks wherein states of nodes can be changed by various influence mechanisms. They extend their model for advertising with multiple rating levels.
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