A Comparative Analysis on Influence Maximization Models in Social Networks

Influence Maximization (IM) models in social networks are used to find the most influential nodes present in the network to evaluate the information propagation caused by them, upon activation. Among the available models, three popular models: Greedy, Cost Effective Lazy Forward (CELF) and CELF++ have gained researchers’ attention due to their efficiency and effectiveness. This research aims to perform a comparative analysis of the performance of these models on various real-world social and complex networks. The results reveal that CELF++ could solve the IM problem more effectively than the other two algorithms. The obtained inferences along with the limitations are presented in this paper.