An improved clustering based multi-objective evolutionary algorithm for influence maximization under variable-length solutions

Abstract

Knowledge of influential actors on real networks is essential for designing policies for boosting the spread of positive things (e.g., news and innovation) and preventing the spread of negative things (e.g., disease and misinformation). Finding an optimum set of nodes with maximum influence, known as Influence Maximization (IM), aims to achieve this, which requires consideration of both magnitude of the influence spread and other important factors such as the cost and size of the seed set. However, most previous research has viewed the IM problem as a single objective problem, wherein decision-makers are required to make decisions on other criteria without having a clear view of them. In this study, the IM problem is formulated as a Multi-objective Influence Maximization Problem (MIMP) with three conflicting objectives and real-world constraints. An improved version of θ-Dominance based Evolutionary Algorithm, named as θDEA-II, is proposed to solve the MIMP. To achieve a better balance between divergence and convergence of the proposed θDEA-II, a gap-based selection operator is introduced along with some modifications in the sorting and normalization process. Moreover, because the proposed MIMP deals with variable-length solutions, all of the investigated algorithms, including the baseline algorithms, are constructed with a novel crossover mechanism capable of producing offsprings that are different in size from their parents. Experimental results on six real-world datasets demonstrate that the proposed MIMP formulation as well as the θDEA-II algorithm are effective for finding an optimal solution.