Enhancing Information Diffusion in Online Social Networks: A Study on Influential Node Identification and Its Applications

Social networks connect numerous people within a short period of time and this property attracts many marketing companies and organizations to promote their products. A major research problem in online social networks is Influential node identification which has a large number of ties in the network. Influence maximization finds an influential node and maximizes information diffusion. Organizations started to use information diffusion features in marketing to improve their product promotion. To achieve influence maximization approximation algorithms and diffusion models are widely used. Influence maximization selects the initial users to effectively diffuse the information to the massive quantity of users in a social network. A greedy algorithm was introduced to discover the information hub effectively. It consists of two diffusion models, namely the Independent Cascade Model (IC) Linear Threshold Model (LT). IC is one of the influence diffusion models. In this model, every activated node gets a single chance to change the state of the inactive neighbor nodes. The LT model mainly focuses on the threshold behavior in influence diffusion. The existing methods consider the degree and structure of the network in influential node identification. Most of the existing works consider the number of nearest neighbors, and the user’s connectivity based on the user’s rating. Influential nodes are mainly used in marketing and also used in various applications such as public opinion, healthcare, communication, education, agriculture, and epidemiology. This work presents a survey of ways to achieve influential maximization in a large-scale social network. Therefore the need for efficient methods to find influential nodes and influence maximization has great importance in the near future.