Non-Markovian models of opinion dynamics on temporal networks

TL;DR

This paper introduces non-Markovian opinion dynamics models on temporal networks with arbitrary waiting-time distributions, analyzing their convergence and steady-state behavior, highlighting the impact of heavy-tailed distributions on opinion formation.

Contribution

It develops a general framework for non-Markovian opinion models driven by arbitrary waiting-time distributions and analyzes their convergence properties and steady states.

Findings

Heavy-tailed WTDs slow convergence to consensus.

Entities with larger waiting times influence steady-state opinions more.

All homogeneous models converge to the same steady state regardless of WTD.

Abstract

Traditional models of opinion dynamics, in which the nodes of a network change their opinions based on their interactions with neighboring nodes, consider how opinions evolve either on time-independent networks or on temporal networks with edges that follow Poisson statistics. Most such models are Markovian. However, in many real-life networks, interactions between individuals (and hence the edges of a network) follow non-Poisson processes and thus yield dynamics with memory-dependent effects. In this paper, we model opinion dynamics in which the entities of a temporal network interact and change their opinions via random social interactions. When the edges have non-Poisson interevent statistics, the corresponding opinion models are have non-Markovian dynamics. We derive an opinion model that is governed by an arbitrary waiting-time distribution (WTD) and illustrate a variety of induced opinion models from common WTDs (including Dirac delta distributions, exponential distributions, and heavy-tailed distributions). We analyze the convergence to consensus of these models and prove that homogeneous memory-dependent models of opinion dynamics in our framework always converge to the same steady state regardless of the WTD. We also conduct a numerical investigation of the effects of waiting-time distributions on both transient dynamics and steady states. We observe that models that are induced by heavy-tailed WTDs converge to a steady state more slowly than those with light tails (or with compact support) and that entities with larger waiting times exert a larger influence on the mean opinion at steady state.