Selecting a Cost-Effective Seed for Maximizing the Social Influence Under Real-life Constraints

Abstract

The Influence Maximization (IM) problem aims at maximizing the diffusion of information or adoption of products among users in a social network by identifying and activating a set of initial users. In real-life applications, it is not unrealistic to have a higher activation cost for a user with higher influence. However, the existing works on IM consider finding the most influential users as the seed set, ignoring either the activation costs of such individual nodes and the total budget or the size of the seed set, which may not be always an optimal solution, particularly from the financial and managerial perspectives, respectively. To address these issues, we propose a more realistic and generalized formulation termed as multi-constraint influence maximization (MCIM) aiming to achieve a cost-effective solution under both budgetary and cardinality constraints. Unlike the existing IM formulations, the proposed MCIM is no longer a monotone but a submodular function. As it is also proved to be an NP-hard problem, we propose a simple additive weighting (SAW) assisted differential evolution (DE) algorithm for solving the large-size real-world problems. Experimental results on four real-world datasets show that the proposed formulation and algorithm are effective in finding a cost-effective seed set.