Generalized Opinion Dynamics Model for Social Trust Networks in Opinion Maximization

TL;DR

This paper introduces a generalized opinion dynamics model (GODM) for social trust networks that optimizes overall expressed opinions by considering social status and evaluations, with proven optimal algorithms and superior experimental results.

Contribution

The paper presents a novel opinion dynamics model with an interpretable confidence index and an optimal algorithm for opinion maximization in social trust networks.

Findings

The proposed method outperforms state-of-the-art in four datasets.

Average benefit improvements range from 31.5% to 83.2%.

The model provides an analytic solution for Nash equilibrium in opinion maximization.

Abstract

In this paper, we propose a generalized opinion dynamics model (GODM), which can dynamically compute each person's expressed opinion, to solve the internal opinion maximization problem for social trust networks. In the model, we propose a new, reasonable and interpretable confidence index, which is determined by both person's social status and the evaluation around him. By using the theory of diagonally dominant, we obtain the optimal analytic solution of the Nash equilibrium with maximum overall opinion. We design a novel algorithm to maximize the overall with given budget by modifying the internal opinions of people in the social trust network, and prove its optimality both from the algorithm itself and the traditional optimization algorithm-ADMM algorithms with $l_1$-regulations. A series of experiments are conducted, and the experimental results show that our method is superior to the state-of-the-art in four datasets. The average benefit has promoted $67.5\%$, $83.2\%$, $31.5\%$, and $33.7\%$ on four datasets, respectively.