Robustness of the 2-Choices Dynamics to Node Failures

TL;DR

This paper examines how the classical 2-choices distributed consensus algorithm maintains robustness against node failures, identifying thresholds where the majority opinion is preserved or lost, with implications for network reliability.

Contribution

It introduces a threshold-based analysis of the 2-choices dynamics under node failures, revealing phase transitions in opinion stability on different graph structures.

Findings

Majority opinion persists exponentially long when failure probability is below threshold.

Above the threshold, opinions quickly become evenly split, losing majority support.

The results apply to complete and expander graphs with spectral gap conditions.

Abstract

In many applications, it becomes necessary for a set of distributed network nodes to agree on a common value or opinion as quickly as possible and with minimal communication overhead. The classical 2-choices rule is a well-known distributed algorithm designed to achieve this goal. Under this rule, each node in a network updates its opinion at random instants by sampling two neighbours uniformly at random and then adopting the common opinion held by these neighbours if they agree. For a sufficiently well-connected network of $n$ nodes and two initial opinions, this simple rule results in the network being absorbed in a consensus state in $O(\log n)$ time (with high probability) and the consensus is obtained on the opinion held by the majority of nodes initially. In this paper, we study the robustness of this algorithm to node failures. In particular, we assume that with a constant probability $\alpha$, a node may fail to update according to the 2-choices rule and erroneously adopt any one of the two opinions uniformly at random. This is a strong form of failure under which the network can no longer be absorbed in a consensus state. However, we show that as long as the error probability $\alpha$ is less than a threshold value, the network is able to retain the majority support of the initially prevailing opinion for an exponentially long time ($\Omega(\poly(\exp(n)))$). In contrast, when the error probability is above a threshold value, we show that any opinion quickly ($O(\log n)$ time) loses its majority support and the network reaches a state where (nearly) an equal proportion of nodes support each opinion. We establish the above phase transition in the dynamics for both complete graphs and expander graphs with sufficiently large spectral gaps and sufficiently homogeneous degrees. Our analysis combines spectral graph theory with Markov chain mixing and hitting time analyses.