Intelligent Viral Marketing Algorithm over Online Social Network

As the online social network become increasingly popular nowadays, performing viral marketing over it has become the focus of many marketing management. How to select a fixed number of initial users from the total population with the purpose of maximizing the profits has long been open as a typical discrete approximation problem. However most of the existing solutions under the setting of online social network tried to traverse every node using network properties which is time-consuming and ineffective. This paper attacks the problem successfully by implementing intelligent algorithms such as GA, DE, PSO. Considering of the huge search space, we sharply decrease the scalability of the network through analyzing the datasets and sampling the data according to a power law property. Experiment results showed that the model we designed for solving viral marketing problem outperform other current search methods.