  In this paper, we study the Budgeted Influence Maximization with Delay
Problem, for which the number of literature are limited. We propose an
approximate marginal spread computation\mbox{-}based approach for solving this
problem. The proposed methodology has been implemented with three benchmark
social network datasets and the obtained results are compared with the existing
methods from the literature. Experimental results show that the proposed
approach is able to select seed nodes which leads to more number of influential
nodes with reasonable computational time.
