Heterogenous mean-field analysis of a generalized voter-like model on networks

TL;DR

This paper introduces a generalized heterogeneous mean-field framework for analyzing voter models on complex networks, unifying existing processes and enabling estimation of key quantities like fixation time using network statistics.

Contribution

It presents a novel, unified HMF approach for voter models that incorporates heterogeneity and degree-selectivity, extending previous models and simplifying analysis.

Findings

Provides estimates for fixation probability and time based on network properties.

Unifies various voter-like processes under a single generalized framework.

Applicable even when exact solutions are difficult to derive.

Abstract

We propose a generalized framework for the study of voter models in complex networks at the the heterogeneous mean-field (HMF) level that (i) yields a unified picture for existing copy/invasion processes and (ii) allows for the introduction of further heterogeneity through degree-selectivity rules. In the context of the HMF approximation, our model is capable of providing straightforward estimates for central quantities such as the exit probability and the consensus/fixation time, based on the statistical properties of the complex network alone. The HMF approach has the advantage of being readily applicable also in those cases in which exact solutions are difficult to work out. Finally, the unified formalism allows one to understand previously proposed voter-like processes as simple limits of the generalized model.