# Revealing graph bandits for maximizing local influence

arXiv: https://arxiv.org/abs/2605.00489 · HTML: https://mlsanity.com/paper/2605.00489/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 33 references that are in the map.
- First posted on arXiv 1 May 2026.

## Builds on (most cited first, 13 of 13)

- [Online Clustering of Bandits](https://mlsanity.com/paper/1401.8257/) (2014)
- [Online Learning with Feedback Graphs: Beyond Bandits](https://mlsanity.com/paper/1502.07617/) (2015)
- [Combinatorial Multi-Armed Bandit and Its Extension to Probabilistically Triggered Arms](https://mlsanity.com/paper/1407.8339/) (2014)
- [A Gang of Bandits](https://mlsanity.com/paper/1306.0811/) (2013)
- [Unimodal Bandits: Regret Lower Bounds and Optimal Algorithms](https://mlsanity.com/paper/1405.5096/) (2014)
- [From Bandits to Experts: A Tale of Domination and Independence](https://mlsanity.com/paper/1307.4564/) (2013)
- [Online Influence Maximization (Extended Version)](https://mlsanity.com/paper/1506.01188/) (2015)
- [Online Learning with Gaussian Payoffs and Side Observations](https://mlsanity.com/paper/1510.08108/) (2015)
- [Influence Maximization with Bandits](https://mlsanity.com/paper/1503.00024/) (2015)
- [Spectral Thompson sampling](https://mlsanity.com/paper/2604.13739/) (2026)
- [Efficient learning by implicit exploration in bandit problems with side observations](https://mlsanity.com/paper/2604.24555/) (2026)
- [Online learning with noisy side observations](https://mlsanity.com/paper/2604.13740/) (2026)
- [Information Gathering in Networks via Active Exploration](https://mlsanity.com/paper/1504.06423/) (2015)

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

- [Bandits on graphs and structures](https://mlsanity.com/paper/2605.03493/) (2026)
- [Learning from Local Walks on Dynamic Graphs with Bandit Feedback](https://mlsanity.com/paper/2607.10571/) (2026)

## Abstract (arXiv)

We study a graph bandit setting where the objective of the learner is to detect the most influential node of a graph by requesting as little information from the graph as possible. One of the relevant applications for this setting is marketing in social networks, where the marketer aims at finding and taking advantage of the most influential customers. The existing approaches for bandit problems on graphs require either partial or complete knowledge of the graph. In this paper, we do not assume any knowledge of the graph, but we consider a setting where it can be gradually discovered in a sequential and active way. At each round, the learner chooses a node of the graph and the only information it receives is a stochastic set of the nodes that the chosen node is currently influencing. To address this setting, we propose BARE, a bandit strategy for which we prove a regret guarantee that scales with the detectable dimension, a problem dependent quantity that is often much smaller than the number of nodes.