Reinforcement-Learning-Based Competitive Opinion Maximization Approach in Signed Social Networks
Qiang He, Xingwei Wang, Yong Zhao, Bo Yi, Xijia Lu, Mingzhou Yang, Min Huang
Abstract
Competitive opinion maximization (COM) in signed social networks targets at selecting a subset of influential individuals (i.e., seed nodes), spreading the desired opinions of the product to their neighbors against its opponents, and eventually achieving the maximum opinion propagation. Current studies mainly focus on competitive influence maximization and opinion maximization. However, COM in signed social networks has not been studied in depth. In this article, we study the COM in signed social networks and propose a novel reinforcement-learning-based opinion maximization framework (RLOM) to solve the COM problem. The proposed RLOM is composed of two phases: the activated dynamic opinion model and the reinforcement-learning-based seeding process. We theoretically prove the COM problem to be NP-hard. To model the opinion propagation process, we propose the activated dynamic opinion model based on a stateless Q-learning approach. Moreover, we propose the reinforcement-learning-based seeding scheme, which is leveraged in an unknown opponent strategy. Experiment results verify the effectiveness of our method in terms of effective opinions on three signed datasets.