# Graph Neural Bandits

arXiv: https://arxiv.org/abs/2308.10808 · HTML: https://mlsanity.com/paper/2308.10808/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 43 references that are in the map.
- First posted on arXiv 21 Aug 2023.

## Builds on (most cited first, 29 of 29)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [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)
- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020)
- [Graph Convolutional Neural Networks for Web-Scale Recommender Systems](https://mlsanity.com/paper/1806.01973/) (2018)
- [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)
- [Neural Graph Collaborative Filtering](https://mlsanity.com/paper/1905.08108/) (2019)
- [FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling](https://mlsanity.com/paper/1801.10247/) (2018)
- [Fine-tuning CNN Image Retrieval with No Human Annotation](https://mlsanity.com/paper/1711.02512/) (2017)
- [Few-Shot Learning with Graph Neural Networks](https://mlsanity.com/paper/1711.04043/) (2017)
- [Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networks](https://mlsanity.com/paper/1905.13210/) (2019)
- [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)
- [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)
- [A Gang of Bandits](https://mlsanity.com/paper/1306.0811/) (2013)
- [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)
- [Neural Bandit with Arm Group Graph](https://mlsanity.com/paper/2206.03644/) (2022)
- [Neural Collaborative Filtering Bandits via Meta Learning](https://mlsanity.com/paper/2201.13395/) (2022)

## Built on by (most cited first, 20 of 20)

- [Discrete-state Continuous-time Diffusion for Graph Generation](https://mlsanity.com/paper/2405.11416/) (2024)
- [How Efficient is LLM-Generated Code? A Rigorous & High-Standard Benchmark](https://mlsanity.com/paper/2406.06647/) (2024)
- [VCR-Graphormer: A Mini-batch Graph Transformer via Virtual Connections](https://mlsanity.com/paper/2403.16030/) (2024)
- [Towards Unified Multi-Modal Personalization: Large Vision-Language Models for Generative Recommendation and Beyond](https://mlsanity.com/paper/2403.10667/) (2024)
- [Transformer Copilot: Learning from The Mistake Log in LLM Fine-tuning](https://mlsanity.com/paper/2505.16270/) (2025)
- [BACKTIME: Backdoor Attacks on Multivariate Time Series Forecasting](https://mlsanity.com/paper/2410.02195/) (2024)
- [Adaptive Batch-Wise Sample Scheduling for Direct Preference Optimization](https://mlsanity.com/paper/2506.17252/) (2025)
- [PageRank Bandits for Link Prediction](https://mlsanity.com/paper/2411.01410/) (2024)
- [Can Graph Neural Networks Learn Language with Extremely Weak Text Supervision?](https://mlsanity.com/paper/2412.08174/) (2024)
- [Contextual Rollout Bandits for Reinforcement Learning with Verifiable Rewards](https://mlsanity.com/paper/2602.08499/) (2026)
- [Neural Contextual Bandits for Personalized Recommendation](https://mlsanity.com/paper/2312.14037/) (2023)
- [Neural Active Learning Beyond Bandits](https://mlsanity.com/paper/2404.12522/) (2024)
- [Meta Clustering of Neural Bandits](https://mlsanity.com/paper/2408.05586/) (2024)
- [Invariant Link Selector for Spatial-Temporal Out-of-Distribution Problem](https://mlsanity.com/paper/2505.24178/) (2025)
- [Influence Maximization via Graph Neural Bandits](https://mlsanity.com/paper/2406.12835/) (2024)
- [Online Clustering of Dueling Bandits](https://mlsanity.com/paper/2502.02079/) (2025)
- [Leveraging heterogeneous spillover in maximizing contextual bandit rewards](https://mlsanity.com/paper/2310.10259/) (2023)
- [Graph Neural Thompson Sampling](https://mlsanity.com/paper/2406.10686/) (2024)
- [Learning Peer Influence Probabilities with Linear Contextual Bandits](https://mlsanity.com/paper/2510.19119/) (2025)
- [The Bandit's Blind Spot: The Critical Role of User State Representation in Recommender Systems](https://mlsanity.com/paper/2604.26651/) (2026)

## Abstract (arXiv)

Contextual bandits algorithms aim to choose the optimal arm with the highest reward out of a set of candidates based on the contextual information. Various bandit algorithms have been applied to real-world applications due to their ability of tackling the exploitation-exploration dilemma. Motivated by online recommendation scenarios, in this paper, we propose a framework named Graph Neural Bandits (GNB) to leverage the collaborative nature among users empowered by graph neural networks (GNNs). Instead of estimating rigid user clusters as in existing works, we model the "fine-grained" collaborative effects through estimated user graphs in terms of exploitation and exploration respectively. Then, to refine the recommendation strategy, we utilize separate GNN-based models on estimated user graphs for exploitation and adaptive exploration. Theoretical analysis and experimental results on multiple real data sets in comparison with state-of-the-art baselines are provided to demonstrate the effectiveness of our proposed framework.