Opinion Control under Adversarial Network Perturbation: A Stackelberg Game Approach

TL;DR

This paper models and analyzes the impact of adversarial network perturbations on opinion dynamics in social networks, proposing a Stackelberg game approach to control opinions under malicious influence.

Contribution

It introduces a Stackelberg game framework for opinion control considering adversarial perturbations, extending existing models to dynamic and malicious network influences.

Findings

Adversarial perturbations significantly alter opinion formation.

The proposed algorithm effectively mitigates malicious influence.

Simulations validate the model's accuracy and control strategy effectiveness.

Abstract

The emerging social network platforms enable users to share their own opinions, as well as to exchange opinions with others. However, adversarial network perturbation, where malicious users intentionally spread their extreme opinions, rumors, and misinformation to others, is ubiquitous in social networks. Such adversarial network perturbation greatly influences the opinion formation of the public and threatens our societies. Thus, it is critical to study and control the influence of adversarial network perturbation. Although tremendous efforts have been made in both academia and industry to guide and control the public opinion dynamics, most of these works assume that the network is static, and ignore such adversarial network perturbation. In this work, based on the well-accepted Friedkin-Johnsen opinion dynamics model, we model the adversarial network perturbation and analyze its impact on the networks' opinion. Then, from the adversary's perspective, we analyze its optimal network perturbation, which maximally changes the network's opinion. Next, from the network defender's perspective, we formulate a Stackelberg game and aim to control the network's opinion even under such adversarial network perturbation. We devise a projected subgradient algorithm to solve the formulated Stackelberg game. Extensive simulations on real social networks validate our analysis of the adversarial network perturbation's influence and the effectiveness of the proposed opinion control algorithm.