Exploring the effect of dynamic seed activation in social networks

Abstract

In this paper, we address the problem of maximizing the influence in a social network by hiring a few users in the network to propagate the information. Considering limited budget and time, hired users (seeds) are activated dynamically at different time intervals over a time horizon. This motivates to avoid the same seed activation in consecutive time intervals that leads to deteriorating the seeds’ efficiency. The aim of this paper is to maximize the total gain obtained in the process of maximizing the influence in a social network. Total gain is obtained by earning of influencing the customers and then deducting the cost incurred for influencing the users. Therefore, an improvised memetic algorithm is developed to find the seeds that are to be activated at different time intervals to maximize the gain. Experimental results validate the effectiveness of the proposed algorithm, and it is found to perform better in identifying the potential seeds with minimum expenditure.