mlsanityData to 14 Oct 2026

Plurality Consensus in the Gossip Model

arXiv 1407.2565 ↗First posted 9 Jul 2014cs.DCcited by 1

Not on the map. Why?

Abstract (arXiv)

We study Plurality Consensus in the Gossip Model over a network of n anonymous agents. Each agent supports an initial opinion or color. We assume that at the onset, the number of agents supporting the plurality color exceeds that of the agents supporting any other color by a sufficiently-large bias. The goal is to provide a protocol that, with high probability, brings the system into the configuration in which all agents support the (initial) plurality color. We consider the Undecided-State Dynamics, a well-known protocol which uses just one more state (the undecided one) than those necessary to store colors. We show that the speed of convergence of this protocol depends on the initial color configuration as a whole, not just on the gap between the plurality and the second largest color community. This dependence is best captured by a novel notion we introduce, namely, the monochromatic distance md(barc) which measures the distance of the initial color configuration bar mathbf c from the closest monochromatic one. In the complete graph, we prove that, for a wide range of the input parameters, this dynamics converges within O(md(bar mathbf c) log n) rounds. We prove that this upper bound is almost tight in the strong sense: Starting from any color configuration bar mathbf c, the convergence time is Omega(md(bar mathbf c)). Finally, we adapt the Undecided-State Dynamics to obtain a fast, random walk-based protocol for plurality consensus on regular expanders. This protocol converges in O(md(bar mathbf c) polylog(n)) rounds using only polylog(n) local memory. A key-ingredient to achieve the above bounds is a new analysis of the maximum node congestion that results from performing n parallel random walks on regular expanders. All our bounds hold with high probability.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). First posted on arXiv 9 Jul 2014.

Builds on

No reference list yet.

Built on by

Cited by year

Citing papers per year: 2023: 1.

1 paper.