Influence Maximization in Social Networks Using an Improved Multi-Objective Particle Swarm Optimization

The influence maximization (IM) problem has received great attention in the field of social network analysis, and its analysis results can provide reliable basis for decision makers when promoting products or political viewpoints. IM problem aims to select a set of seed users from social networks and maximize the number of users expected to be influenced. Most previous studies on the IM problem focused only on the single-objective problem of maximizing the influence spread of the seed set, ignoring the cost of the seed set, which causes decision makers to be unable to develop effective management strategies. In this work, the IM problem is formulated as a multi-objective IM problem that considers the cost of the seed set. An improved multi-objective particle swarm optimization (IMOPSO) algorithm is proposed to solve this problem. In the IMOPSO algorithm, the initialization strategy of Levy flight based on degree value is used to improve the quality of the initial solution, and the local search strategy based on greedy mechanism is designed to improve the Pareto Frontier distribution and promote algorithm convergence. Experimental results on six real social networks demonstrate that the proposed IMOPSO algorithm is effective, reducing runtime while providing competitive solutions.