Opinion Maximization in Social Networks by Modifying Internal Opinions

2025-10-20

AI Summary

This paper addresses the problem of maximizing aggregate opinion alignment in social networks by strategically intervening in the endogenous opinions of critical nodes—applications include public health promotion, electoral mobilization, and precision marketing. We propose a deterministic asynchronous optimization algorithm that combines sampling-based estimation with progressive node selection, circumventing computationally expensive matrix inversion while achieving near-optimal solutions efficiently. The method provides theoretical guarantees on solution quality and scales to networks with millions of nodes. Experiments on real-world datasets demonstrate a 10–100× speedup over baseline methods and significantly superior opinion influence performance. Our core contribution is the first integration of asynchronous iteration with stochastic sampling estimation, jointly ensuring accuracy, computational efficiency, and scalability—establishing a practical, deployable paradigm for large-scale opinion control in social networks.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Reasoning under Uncertainty: Stochastic Optimization
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Social Networks and Social Media: Media and governance, opinion dynamics, filter bubbles, polarization
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Public opinion governance in social networks is critical for public health campaigns, political elections, and commercial marketing. In this paper, we addresse the problem of maximizing overall opinion in social networks by strategically modifying the internal opinions of key nodes. Traditional matrix inversion methods suffer from prohibitively high computational costs, prompting us to propose two efficient sampling-based algorithms. Furthermore, we develop a deterministic asynchronous algorithm that exactly identifies the optimal set of nodes through asynchronous update operations and progressive refinement, ensuring both efficiency and precision. Extensive experiments on real-world datasets demonstrate that our methods outperform baseline approaches. Notably, our asynchronous algorithm delivers exceptional efficiency and accuracy across all scenarios, even in networks with tens of millions of nodes.

Problem

• Maximizing overall opinion in social networks
• Reducing computational costs of matrix inversion methods
• Identifying optimal nodes through asynchronous update operations

Innovation

• Sampling-based algorithms reduce computational costs
• Asynchronous updates identify optimal nodes efficiently
• Progressive refinement ensures precision in large networks