Asymptotic analysis of threshold models for social networks

TL;DR

This paper introduces a dynamic threshold model for social networks, analyzing how collective actions evolve based on agent confidence and network topology, with comprehensive asymptotic behavior characterization.

Contribution

It provides a complete analytical characterization of the asymptotic behaviors of the model across three different network topologies.

Findings

Asymptotic activity patterns depend on self-confidence and initial thresholds.

Behavior varies significantly with network topology.

Radical agents influence collective dynamics based on initial conditions.

Abstract

A class of dynamic threshold models is proposed, for describing the upset of collective actions in social networks. The agents of the network have to decide whether to undertake a certain action or not. They make their decision by comparing the activity level of their neighbors with a time-varying threshold, evolving according to a time-invariant opinion dynamic model. Key features of the model are a parameter representing the degree of self-confidence of the agents, and the mechanism adopted by the agents to evaluate the activity level of their neighbors. The case in which a radical agent, initially eager to undertake the action, interacts with a group of ordinary agents, is considered. The main contribution of the paper is the complete analytic characterization of the asymptotic behaviors of the network, for three different graph topologies. The asymptotic activity patterns are determined as a function of the self-confidence parameter and of the initial threshold of the ordinary agents.