A two-stage VIKOR assisted multi-operator differential evolution approach for Influence Maximization in social networks

Abstract

The impact of online social networking on daily life is extending beyond personal boundaries, becoming a tool for financial activities and even public well-being. Interactions among users and items, as nodes of such networks, and flows of information among them play a critical role in this regard. Identifying a small subset of such nodes with maximum spreading power that facilitates this process has attracted a lot of attention. The existing solution approaches to this NP-hard combinatorial optimization problem, known as the Influence Maximization (IM) problem, mainly suffer either from poor solution quality or large computational time. This study addresses these issues by proposing a two-stage optimization framework. First, to reduce the computation complexity, a multi-criteria decision making (MCDM) approach is employed to prune insignificant nodes from the candidate nodes. Second, to optimize the solutions, a modified differential evolution (DE) algorithm equipped with multiple search operators is proposed. The proposed algorithm uses the Expected Diffusion Value (EDV) in place of traditional time-consuming simulations to evaluate the fitness of a candidate solution. We proved that EDV is a monotone and submodular function, and showed that it can effectively and efficiently be integrated into a greedy-based algorithmic framework. We also provided the theoretical analysis and evaluated the performance of this proposed algorithm experimentally using two synthetic and eight diverse real-life networks. The experimental results show that our proposed algorithm achieves a better trade-off between solution quality and running time compared to the existing baseline algorithms.