Fast Computation and Optimization for Opinion-Based Quantities of Friedkin-Johnsen Model

Haoxin Sun, Yubo Sun, Xiaotian Zhou, Zhongzhi Zhang

Keywords: forest matrix, fast algorithm, FJ model, opinion dynamics，optimization problems

TL;DR: We develop sublinear-time algorithms for computing and optimizing opinion-based quantities in the FJ model using partial rooted forest sampling.

Abstract: In this paper, we address the problem of fast computation and optimization of opinion-based quantities in the Friedkin–Johnsen (FJ) model. We first introduce the concept of partial rooted forests and present an efficient algorithm for computing these quantities using this method. Furthermore, we study two optimization problems in the FJ model: the Opinion Minimization Problem and the Polarization and Disagreement Minimization Problem. For both problems, we propose fast algorithms based on partial rooted forest sampling. Our methods reduce the time complexity from linear to sublinear. Extensive experiments on real-world networks demonstrate that our algorithms are both accurate and efficient, outperforming state-of-the-art methods and scaling effectively to large-scale networks.

Primary Area: Social and economic aspects of machine learning (e.g., fairness, interpretability, human-AI interaction, privacy, safety, strategic behavior)

Submission Number: 10002