Majority Dynamics on Assortative Sparse Stochastic Block Models

2026-07-27

AI Summary

This study analyzes the convergence behavior of majority dynamics on resampled sparse binary stochastic block models, where intra-community and inter-community edge probabilities are given by $a\log N/N$ and $b\log N/N$, respectively, with $a > b > 1$. The work introduces a weighted advantage metric $\widetilde{\Delta}_t = b|B_t| - a|R_t|$ as the key determinant of convergence speed, demonstrating that it dominates over initial numerical advantage. By deriving sharp estimates for single-vertex flip probabilities on sparse graphs and combining high-probability asymptotic analysis with a ReLU-type rate exponent formula, the paper characterizes three distinct convergence time scales: constant steps, $N^{o(1)}$ steps, and $N^{I_0 + o(1)}$ steps. Precise threshold conditions and matching upper and lower bounds are established, with all results holding with high probability.

Abstract

Majority dynamics is a two-opinion process in which each vertex repeatedly updates to the majority opinion among its neighbors. We study this process on a resampled sparse binary stochastic block model in the assortative regime. At each time step, a graph is sampled from the current opinion partition: vertices with the same opinion are joined with probability $α=a\log N/N$, while vertices with differing opinions are joined with probability $β=b\log N/N$, where $a>b>1$. Let $B_t$ and $R_t$ denote the blue and red camps at time $t$. We show that the weighted advantage $\widetildeΔ_t =b|B_t|-a|R_t|$, rather than the unweighted advantage $Δ_t=|B_t|-|R_t|$ alone, governs the pace to unanimity. Our results, which hold with high probability as \(N\to\infty\), identify three regimes for blue unanimity under the initial blue advantage, i.e., $Δ_0>0$: constant time, subpolynomial time, and polynomial time. First, when $\widetildeΔ_0 \gtrsim -N/\sqrt{\log N}$, blue unanimity occurs within three updates. Second, when $\widetildeΔ_0 < 0$ and $|\widetildeΔ_0| = o(N)$, blue unanimity occurs within $N^{o(1)}$ updates. Furthermore, when $\widetildeΔ_0 < 0$, $|\widetildeΔ_0| = O(N)$, and $Δ_0\gg\sqrt{N/\log N}$, blue unanimity still occurs within $N^{I_0+o(1)}$ updates, where \[ I_0= \left(\mathbf{ReLU}\Big(\sqrt{a\frac{|R_0|}{N}}-\sqrt{b\frac{|B_0|}{N}}\Big)\right)^2, \] and $\mathbf{ReLU}(x)=\max\{x,0\}$. Conversely, away from the weighted threshold, when $|B_0|/|R_0|\le a/b-κ$ and $Δ_0>0$, $N^{I_0 - o(1)}$ updates are necessary for blue unanimity. Our analysis relies on detailed estimates for one-vertex flip probabilities in sparse binomial differences, which could be of independent interest.

Problem

• Majority Dynamics
• Stochastic Block Model
• Assortative Networks
• Opinion Consensus
• Sparse Graphs

Innovation

• majority dynamics
• stochastic block model
• weighted advantage
• sparse graphs
• consensus time