An Approximate Marginal Spread Computation Approach for the Budgeted Influence Maximization with Delay

TL;DR

This paper introduces an approximate method for influence maximization with delay constraints, effectively selecting influential nodes in social networks while balancing computational efficiency.

Contribution

It proposes a novel approximate marginal spread computation approach for the budgeted influence maximization with delay, validated on benchmark datasets.

Findings

Outperforms existing methods in influence spread

Achieves higher influence with reasonable computation time

Effective on multiple social network datasets

Abstract

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.