Identification Important Nodes in Diffusion Process by User Experience on Social Network

Abstract

The influence maximization problem (IMP) has been proposed in social networks. Nowadays, it is considered an important and practical problem due to the earnings potential by identifying a set of influential nodes, and therefore, it has been attracted by many researchers. This problem seeks to identify a set with K nodes among the social network nodes to maximize the influence and diffusion of information in that community. Algorithms proposed by other researchers have many shortcomings in terms of accuracy and run time of the algorithm. Hence, this article aimed to find the best, most accurate, and fastest solution to the problem.The article presented the UXM algorithm and used the User Experience criterion for the first time to solve this problem. At first, taking into account the reach club phenomenon and using criteria such as clustering coefficient, degree and also using user experience, nodes with more influence have been selected as the primary candidate set. Then, according to the component nodes, K final influential nodes have been selected. In this way, it could identify the set of nodes as accurately as possible with high efficiency in the shortest possible time. The evaluation of this algorithm and its comparison with other algorithms indicated excellent results in terms of run time and accuracy in selecting the set of nodes by the proposed algorithm.