Abstract
Social media is a widely used internet-based form of communication. This platform allows users to make conversations, share images, and videos and produce web content. To increase the strength of influence between individuals, we require tracking the influential nodes in the dynamic social network. Finding highly influential nodes is of interest to managers and analysts who work with social networks. The company marketing heads may want to detect the influential people in the network to afford them a discount or free product trust and have to sell with their friends to buy the product. The traditional system for influence maximization cannot find those influential users, to increase the influences in the network. The influential node tracking (INT) problems were estimated. The main aim of INT is to track the set of influence maximization. For tracking the influential node in social media, we proposed a novelty-based tuned linear threshold model (TLTM) algorithm. The evaluation results that proved the proposed CDR Dataset clearly expressed the efficiency and effectiveness of all proposed algorithms.
Published Version
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