Generalized Dynamics in Social Networks With Antagonistic Interactions

TL;DR

This paper presents a comprehensive nonlinear model of opinion dynamics in social networks with both antagonistic interactions and state-dependent susceptibility, analyzing convergence and opinion levels through advanced matrix theory.

Contribution

It introduces a generalized nonlinear opinion dynamics model considering various susceptibility scenarios and provides theoretical conditions for convergence in signed social networks.

Findings

States converge into a subspace defined by the positive eigenvector

Different opinion levels can be characterized by eigenvector entries

Theoretical results are validated through illustrative examples

Abstract

In this paper, we investigate a general nonlinear model of opinion dynamics in which both state-dependent susceptibility to persuasion and antagonistic interactions are considered. According to the existing literature and socio-psychological theories, we examine three specializations of state-dependent susceptibility, that is, stubborn positives scenario, stubborn neutrals scenario, and stubborn extremists scenario. Interactions among agents form a signed graph, in which positive and negative edges represent friendly and antagonistic interactions, respectively. Based on Perron-Frobenius property of eventually positive matrices and LaSalle invariance principle, we conduct a comprehensive theoretical analysis of the generalized nonlinear opinion dynamics. We obtain some sufficient conditions such that the states of all agents converge into the subspace spanned by the right positive eigenvector of an eventually positive matrix. When there exists at least one entry of the right positive eigenvector which is not equal to one, the derived results can be used to describe different levels of an opinion. Finally, we present two examples to demonstrate the effectiveness of the theoretical findings.