# Optimal Aggregation Strategies for Social Learning over Graphs

arXiv: https://arxiv.org/abs/2203.07065 · HTML: https://mlsanity.com/paper/2203.07065/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Mar 2022.

## Built on by (most cited first, 2 of 2)

- [Large deviations rates for stochastic gradient descent with strongly convex functions](https://mlsanity.com/paper/2211.00969/) (2022)
- [Why Linear Recurrent Memory Works in Partially Observable Reinforcement Learning](https://mlsanity.com/paper/2605.31261/) (2026)

## Abstract (arXiv)

Adaptive social learning is a useful tool for studying distributed decision-making problems over graphs. This paper investigates the effect of combination policies on the performance of adaptive social learning strategies. Using large-deviation analysis, it first derives a bound on the steady-state error probability and characterizes the optimal selection for the Perron eigenvectors of the combination policies. It subsequently studies the effect of the combination policy on the transient behavior of the learning strategy by estimating the adaptation time in the low signal-to-noise ratio regime. In the process, it is discovered that, interestingly, the influence of the combination policy on the transient behavior is insignificant, and thus it is more critical to employ policies that enhance the steady-state performance. The theoretical conclusions are illustrated by means of computer simulations.