Multiscale Voter Model on Real Networks

TL;DR

This paper introduces the Multiscale Voter Model (MVM) to study opinion formation on real networks, revealing how clan influence at different scales affects consensus and polarization.

Contribution

The paper presents a novel multiscale framework using coarse-grained network embeddings to analyze opinion dynamics influenced by group size and influence.

Findings

Transition between consensus and polarization depends on clan scale and influence strength.

Enhancing diversity promotes consensus, while strong kinship leads to opinion clusters.

Spatial patterns in hyperbolic embeddings reveal opinion segregation.

Abstract

We introduce the Multiscale Voter Model (MVM) to investigate clan influence at multiple scale -- family, neighborhood, political party... -- in opinion formation on real complex networks. Clans, consisting of similar nodes, are constructed using a coarse-graining procedure on network embeddings that allows us to control for the length scale of interactions. We ran numerical simulations to monitor the evolution of MVM dynamics in real and synthetic networks, and identified a transition between a final stage of full consensus and one with mixed binary opinions. The transition depends on the scale of the clans and on the strength of their influence. We found that enhancing group diversity promotes consensus while strong kinship yields to metastable clusters of same opinion. The segregated domains, which signal opinion polarization, are discernible as spatial patterns in the hyperbolic embeddings of the networks. Our multiscale framework can be easily applied to other dynamical processes affected by scale and group influence.