Reinforcement-Learning-Based Dynamic Opinion Maximization Framework in Signed Social Networks

Qiang He, Yingjie Lv, Xingwei Wang, Jianhua Li, Min Huang, Lianbo Ma, Yuliang Cai

Abstract: Dynamic opinion maximization (DOM) is a significant optimization issue, whose target is to select some nodes in the network and prorogate the opinions of network nodes, and produce the optimum node opinions. Until now, the node opinions of related researches are unchanged and seldom focus on social relationships. In the real scenario, the dynamic process of network nodes over time and user preference have existed. Therefore, this article proposes the ${Q}$ -learning-based DOM (QDOM) framework in signed social networks to solve the OM problem, which is made up of two phases: 1) the activated dynamic opinion model and 2) the ${Q}$ -learning-based seeding process. We propose the activated dynamic opinion model based on stateless ${Q}$ -learning theory to derive the opinion propagation process. Moreover, we design the ${Q}$ -learning-based seeding algorithm to obtain the seed nodes. The experimental results on the four signed social network data sets demonstrate that the proposed framework outperforms the state-of-the-art approaches on positive opinions, the ratio of positive opinions, and activated nodes.

External IDs:doi:10.1109/tcds.2022.3141952