Opinion Maximization Through Unknown Influence Power in Social Networks Under Weighted Voter Model

Opinion maximization in social networks is an optimization problem, which targets at determining some influential individuals (i.e., seed nodes), propagating the desired opinion to their neighbors, and eventually obtaining maximum opinion spread. Previous studies assume that influence power of one individual is mainly calculated by using some network structure properties and once the opinion of one individual is determined, its opinion usually keeps unchanged. However, in the real scenario, the influence power of one individual may be unknown and should be closely associated with the dynamic opinion formation process. In this paper, we propose a novel Influence Power-based Opinion Framework (IPOF) to solve the opinion maximization problem, which is composed of two phases: 1) influence power estimation, and 2) elimination of influence overlapping (EIO). Specifically, we design the exponential influence power and estimate the unknown parameter of influence power through maximum likelihood estimation due to its simplicity, practicability, and superior convergence in large samples. To generate the opinion series dynamically, the weighted voter model is proposed by leveraging influence power and intimate degree. Moreover, we also prove that the likelihood function is concave by using Hessian matrix. To determine the initial seed nodes and facilitate large opinion propagation, influence power-based EIO algorithm is proposed. Experimental results in six social networks demonstrate that the proposed approach outperforms the state-of-the-art benchmarks.