Identification of influential users in social networks based on users' interest

Ever increasing popularity social networks has attracted many companies to use them for viral marketing. Identification of influential users for spreading news and marketing is a major challenge in viral marketing. In most of the existing influence maximization approaches, the topological locations of nodes on the network has been considered as a criterion to determine their influentiality. They have mainly neglected users interest in the marketing message. Although a number of existing works have considered interests of users, they have not used any criterion to specify the interest. This manuscript proposes a novel criterion to measure the interest of users in the marketing messages. We then propose a novel algorithm to obtain the set of the most influential users. Experimental results on real-world and synthetic networks reveal effectiveness of the proposed method as compared to the existing state-of-the-art influence maximization algorithms.