Influence Maximization Problem Based on Discrete Multi Objective Multi-verse Optimizer

Advertising on social networks is crucial for many businesses, and achieving greater influence spread with less budget spent is seen as an important issue in the field of social networking. Most researchers only focus on how to select the set of nodes to maximize influence, while neglecting the goal of budget minimization. Therefore, a multi-objective function is defined on the basis of considering both influence maximization and budget minimization. When selecting the set of nodes to maximize influence spread, the number of fans is introduced as a metric to calculate the cost budget required for the node set, so as to minimize the budget. To effectively solve multi-objective problems, a Discrete Multi-Objective Multi-Verse optimizer (DMOMVO) is proposed. In DMOMVO, a node-based coding mechanism and evolution rules are constructed. To address the issue of getting stuck in local optima, a monitoring strategy is adopted. Experiments and analysis on three real-world networks under the Independent Cascade model demonstrate that the proposed algorithm can obtain better solutions under complicated situations.