Ignoring Extreme Opinions in Complex Networks: The Impact of Heterogeneous Thresholds

TL;DR

This paper studies opinion dynamics in networks where nodes ignore a certain number of extreme neighbors' opinions, analyzing how individual thresholds affect the likelihood of reaching consensus.

Contribution

It introduces heterogeneous thresholds for ignoring opinions and derives graph conditions and probabilistic guarantees for consensus in such models.

Findings

Consensus is guaranteed under specific graph conditions.

Random networks with distributed thresholds tend to reach consensus asymptotically.

Heterogeneous thresholds influence the opinion convergence process.

Abstract

We consider a class of opinion dynamics on networks where at each time-step, each node in the network disregards the opinions of a certain number of its most extreme neighbors and updates its own opinion as a weighted average of the remaining opinions. When all nodes disregard the same number of extreme neighbors, previous work has shown that consensus will be reached if and only if the network satisfies certain topological properties. In this paper, we consider the implications of allowing each node to have a personal threshold for the number of extreme neighbors to ignore. We provide graph conditions under which consensus is guaranteed for such dynamics. We then study random networks where each node's threshold is drawn from a certain distribution, and provide conditions on that distribution, together with conditions on the edge formation probability, that guarantee that consensus will be reached asymptotically almost surely.