# Distributed Algorithms for Aggregative Games on Graphs

arXiv: https://arxiv.org/abs/1605.00267 · HTML: https://mlsanity.com/paper/1605.00267/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 May 2016.

## Built on by (most cited first, 4 of 4)

- [Competing Adaptive Networks](https://mlsanity.com/paper/2103.15664/) (2021)
- [Global Nash Equilibrium in Non-convex Multi-player Game: Theory and Algorithms](https://mlsanity.com/paper/2301.08015/) (2023)
- [Accelerated Distributed Aggregative Optimization](https://mlsanity.com/paper/2304.08051/) (2023)
- [Diffusion Stochastic Learning Over Adaptive Competing Networks](https://mlsanity.com/paper/2504.19635/) (2025)

## Abstract (arXiv)

We consider a class of Nash games, termed as aggregative games, being played over a networked system. In an aggregative game, a player's objective is a function of the aggregate of all the players' decisions. Every player maintains an estimate of this aggregate, and the players exchange this information with their local neighbors over a connected network. We study distributed synchronous and asynchronous algorithms for information exchange and equilibrium computation over such a network. Under standard conditions, we establish the almost-sure convergence of the obtained sequences to the equilibrium point. We also consider extensions of our schemes to aggregative games where the players' objectives are coupled through a more general form of aggregate function. Finally, we present numerical results that demonstrate the performance of the proposed schemes.