Disassortative mixing accelerates consensus in the naming game

TL;DR

This paper investigates how disassortative degree mixing in networks speeds up consensus formation in the naming game by promoting cluster merging, with supporting analysis of various network dynamics.

Contribution

It reveals that disassortative mixing accelerates consensus in the naming game and provides a qualitative explanation based on cluster statistics.

Findings

Disassortative networks lead to faster convergence.

Disassortative mixing promotes cluster merging.

Other metrics like success rate and word counts are also affected.

Abstract

In this paper, we study the role of degree mixing in the naming game. It is found that consensus can be accelerated on disassortative networks. We provide a qualitative explanation of this phenomenon based on clusters statistics. Compared with assortative mixing, disassortative mixing can promote the merging of different clusters, thus resulting in a shorter convergence time. Other quantities, including the evolutions of the success rate, the number of total words and the number of different words, are also studied.