# Neural Bandit with Arm Group Graph

arXiv: https://arxiv.org/abs/2206.03644 · HTML: https://mlsanity.com/paper/2206.03644/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 50 references that are in the map.
- First posted on arXiv 8 Jun 2022.

## Builds on (most cited first, 28 of 28)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Neural Tangent Kernel: Convergence and Generalization in Neural Networks](https://mlsanity.com/paper/1806.07572/) (2018)
- [Simplifying Graph Convolutional Networks](https://mlsanity.com/paper/1902.07153/) (2019)
- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [Predict then Propagate: Graph Neural Networks meet Personalized PageRank](https://mlsanity.com/paper/1810.05997/) (2018)
- [Representation Learning on Graphs with Jumping Knowledge Networks](https://mlsanity.com/paper/1806.03536/) (2018)
- [A Convergence Theory for Deep Learning via Over-Parameterization](https://mlsanity.com/paper/1811.03962/) (2018)
- [Gradient Descent Finds Global Minima of Deep Neural Networks](https://mlsanity.com/paper/1811.03804/) (2018)
- [Position-aware Graph Neural Networks](https://mlsanity.com/paper/1906.04817/) (2019)
- [Finite-Time Analysis of Kernelised Contextual Bandits](https://mlsanity.com/paper/1309.6869/) (2013)
- [Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels](https://mlsanity.com/paper/1905.13192/) (2019)
- [Neural Thompson Sampling](https://mlsanity.com/paper/2010.00827/) (2020)
- [Multi-armed Bandit Models for the Optimal Design of Clinical Trials: Benefits and Challenges](https://mlsanity.com/paper/1507.08025/) (2015)
- [Online Clustering of Bandits](https://mlsanity.com/paper/1401.8257/) (2014)
- [Optimization of Graph Neural Networks: Implicit Acceleration by Skip Connections and More Depth](https://mlsanity.com/paper/2105.04550/) (2021)
- [Multi-Task Learning for Contextual Bandits](https://mlsanity.com/paper/1705.08618/) (2017)
- [On Context-Dependent Clustering of Bandits](https://mlsanity.com/paper/1608.03544/) (2016)
- [EE-Net: Exploitation-Exploration Neural Networks in Contextual Bandits](https://mlsanity.com/paper/2110.03177/) (2021)
- [Improved Algorithm on Online Clustering of Bandits](https://mlsanity.com/paper/1902.09162/) (2019)
- [Local Clustering in Contextual Multi-Armed Bandits](https://mlsanity.com/paper/2103.00063/) (2021)
- [Multi-facet Contextual Bandits: A Neural Network Perspective](https://mlsanity.com/paper/2106.03039/) (2021)
- [DPPIN: A Biological Repository of Dynamic Protein-Protein Interaction Network Data](https://mlsanity.com/paper/2107.02168/) (2021)
- [From Intrinsic to Counterfactual: On the Explainability of Contextualized Recommender Systems](https://mlsanity.com/paper/2110.14844/) (2021)
- [Contextual Bandit Applications in Customer Support Bot](https://mlsanity.com/paper/2112.03210/) (2021)
- [Neural Collaborative Filtering Bandits via Meta Learning](https://mlsanity.com/paper/2201.13395/) (2022)
- [Online Semi-Supervised Learning with Bandit Feedback](https://mlsanity.com/paper/2010.12574/) (2020)

## Built on by (most cited first, 8 of 8)

- [Graph Neural Bandits](https://mlsanity.com/paper/2308.10808/) (2023)
- [PageRank Bandits for Link Prediction](https://mlsanity.com/paper/2411.01410/) (2024)
- [Improved Algorithms for Neural Active Learning](https://mlsanity.com/paper/2210.00423/) (2022)
- [Neural Contextual Bandits for Personalized Recommendation](https://mlsanity.com/paper/2312.14037/) (2023)
- [Neural Exploitation and Exploration of Contextual Bandits](https://mlsanity.com/paper/2305.03784/) (2023)
- [Meta Clustering of Neural Bandits](https://mlsanity.com/paper/2408.05586/) (2024)
- [Generalized Low-Rank Matrix Contextual Bandits with Graph Information](https://mlsanity.com/paper/2507.17528/) (2025)
- [Graph Neural Thompson Sampling](https://mlsanity.com/paper/2406.10686/) (2024)

## Abstract (arXiv)

Contextual bandits aim to identify among a set of arms the optimal one with the highest reward based on their contextual information. Motivated by the fact that the arms usually exhibit group behaviors and the mutual impacts exist among groups, we introduce a new model, Arm Group Graph (AGG), where the nodes represent the groups of arms and the weighted edges formulate the correlations among groups. To leverage the rich information in AGG, we propose a bandit algorithm, AGG-UCB, where the neural networks are designed to estimate rewards, and we propose to utilize graph neural networks (GNN) to learn the representations of arm groups with correlations. To solve the exploitation-exploration dilemma in bandits, we derive a new upper confidence bound (UCB) built on neural networks (exploitation) for exploration. Furthermore, we prove that AGG-UCB can achieve a near-optimal regret bound with over-parameterized neural networks, and provide the convergence analysis of GNN with fully-connected layers which may be of independent interest. In the end, we conduct extensive experiments against state-of-the-art baselines on multiple public data sets, showing the effectiveness of the proposed algorithm.