# Emergence of correlations in highly biased Consensus Models in seed initial configuration

## TL;DR

This paper investigates how strong bias influences consensus formation in voter models, revealing the emergence of correlations with centrality measures and limitations of existing analytical theories.

## Contribution

It demonstrates through simulations that strong bias leads to correlations between consensus probability and centrality, challenging previous analytical results.

## Key findings

- Strong bias induces correlations with centrality measures.
- Existing theory by Sood et al. breaks down under strong bias.
- Simulations confirm the emergence of new correlation patterns.

## Abstract

We study the consensus probability in Voter Model and Invasion Process starting from a seed initial configuration. In the case where the opinions have the same strength or slightly different (weak bias) this function was computed analytically by Sood, Antal and Redner and depends only on the degree of the promoter individual. We check numerically through large scale simulations the above mentioned theory and we find that in the case of strong bias a correlation between the consensus probability and other centrality measures emerge and Sood et al's theory is broken.