Effects of attachment preferences on coevolution of opinions and networks

TL;DR

This paper studies how different attachment preferences influence the coevolution of opinions and network structures, showing that high-degree-preferential nodes facilitate faster consensus and more heterogeneous networks.

Contribution

It introduces a model analyzing the impact of heterogeneous relinking preferences on opinion convergence and network topology, supported by analytical and simulation results.

Findings

Heterogeneous networks are easier to reach with more high-degree-preferential nodes.

Higher degree distribution heterogeneity accelerates opinion consensus.

Transition point and consensus time depend on the degree distribution's standard deviation.

Abstract

In the coevolution of network structures and opinion formation, we investigate the effects of a mixed population with distinctive relinking preferences on both the convergence time and the network structures. It has been found that a heterogeneous network structure is easier to be reached with more high-degree-preferential(HDP) nodes. There exists high correlation between the convergence time and the network heterogeneity. The heterogeneous degree distribution caused by preferential attachment accelerates the convergence to a consensus state and the shortened convergence time inhibits the occurrence of the following disquieting situation that occurs in a continuously evolving network: with preferential attachment and long-time evolvement, most of the nodes would become separated and only a few leaders would have immediate neighbors. Analytical calculations based on mean field theory reveal that both the transition point ptr and the consensus time tc depend upon the standard deviation of the degree distribution.ptr increases while tc decreases with the rise of it.Functions of ptr and tc are found.Theoretical analyses are in accordance with simulation data.