Opinion Maximization in Social Networks by Modifying Internal Opinions

TL;DR

This paper introduces efficient algorithms for maximizing overall opinion in social networks by strategically modifying key nodes' internal opinions, addressing computational challenges of traditional methods.

Contribution

We propose two sampling-based algorithms and a deterministic asynchronous method that efficiently and accurately identify optimal nodes for opinion maximization in large-scale networks.

Findings

Our methods outperform baseline approaches in real-world datasets.

The asynchronous algorithm achieves high efficiency and accuracy in networks with millions of nodes.

Extensive experiments validate the effectiveness of our algorithms.

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.