# Constructing Stochastic Matrices for Weighted Averaging in Gossip Networks

arXiv: https://arxiv.org/abs/2502.19821 · HTML: https://mlsanity.com/paper/2502.19821/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2025.

## Built on by (most cited first, 1 of 1)

- [Privacy Preserving Gossip Learning](https://mlsanity.com/paper/2609.14778/) (2026)

## Abstract (arXiv)

The convergence of the gossip process has been extensively studied; however, algorithms that generate a set of stochastic matrices, the infinite product of which converges to a rank-one matrix determined by a given weight vector, have been less explored. In this work, we propose an algorithm for constructing (local) stochastic matrices based on a given gossip network topology and a set of weights for averaging across different consensus clusters, ensuring that the gossip process converges to a finite limit set.