mlsanityData to 14 Oct 2026

Graph Neural Bandits

arXiv 2308.10808 ↗First posted 21 Aug 2023cs.LGcited by 20

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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.

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

29 of 43 references.

References · 29

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2018Neural Tangent Kernel: Convergence and Generalization in Neural Networkscited by 2,621
  3. 2019Simplifying Graph Convolutional Networkscited by 1,477
  4. 2020LightGCN: Simplifying and Powering Graph Convolution Network for Recommendationcited by 1,139
  5. 2018Graph Convolutional Neural Networks for Web-Scale Recommender Systemscited by 1,138
  6. 2017Neural Collaborative Filteringcited by 1,111
  7. 2018Predict then Propagate: Graph Neural Networks meet Personalized PageRankcited by 993
  8. 2018Representation Learning on Graphs with Jumping Knowledge Networkscited by 877
  9. 2018A Convergence Theory for Deep Learning via Over-Parameterizationcited by 787
  10. 2019Neural Graph Collaborative Filteringcited by 661
  11. 2018FastGCN: Fast Learning with Graph Convolutional Networks via Importance Samplingcited by 584
  12. 2017Fine-tuning CNN Image Retrieval with No Human Annotationcited by 499
17 more
  1. 2017Few-Shot Learning with Graph Neural Networkscited by 324
  2. 2019Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networkscited by 222
  3. 2019Position-aware Graph Neural Networkscited by 203
  4. 2013Finite-Time Analysis of Kernelised Contextual Banditscited by 177
  5. 2020Neural Thompson Samplingcited by 128
  6. 2015Multi-armed Bandit Models for the Optimal Design of Clinical Trials: Benefits and Challengescited by 114
  7. 2014Online Clustering of Banditscited by 102
  8. 2021Optimization of Graph Neural Networks: Implicit Acceleration by Skip Connections and More Depthcited by 55
  9. 2013A Gang of Banditscited by 49
  10. 2017Multi-Task Learning for Contextual Banditscited by 47
  11. 2016On Context-Dependent Clustering of Banditscited by 44
  12. 2021EE-Net: Exploitation-Exploration Neural Networks in Contextual Banditscited by 44
  13. 2019Improved Algorithm on Online Clustering of Banditscited by 38
  14. 2021Local Clustering in Contextual Multi-Armed Banditscited by 29
  15. 2021Multi-facet Contextual Bandits: A Neural Network Perspectivecited by 20
  16. 2022Neural Bandit with Arm Group Graphcited by 8
  17. 2022Neural Collaborative Filtering Bandits via Meta Learningcited by 7

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 11; 2025: 5; 2026: 2.

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  1. 2024Discrete-state Continuous-time Diffusion for Graph Generationcited by 76
  2. 2024How Efficient is LLM-Generated Code? A Rigorous & High-Standard Benchmarkcited by 43
  3. 2024VCR-Graphormer: A Mini-batch Graph Transformer via Virtual Connectionscited by 34
  4. 2024Towards Unified Multi-Modal Personalization: Large Vision-Language Models for Generative Recommendation and Beyondcited by 25
  5. 2025Transformer Copilot: Learning from The Mistake Log in LLM Fine-tuningcited by 21
  6. 2024BACKTIME: Backdoor Attacks on Multivariate Time Series Forecastingcited by 18
  7. 2025Adaptive Batch-Wise Sample Scheduling for Direct Preference Optimizationcited by 17
  8. 2024PageRank Bandits for Link Predictioncited by 14
  9. 2024Can Graph Neural Networks Learn Language with Extremely Weak Text Supervision?cited by 13
  10. 2026Contextual Rollout Bandits for Reinforcement Learning with Verifiable Rewardscited by 11
  11. 2023Neural Contextual Bandits for Personalized Recommendationcited by 10
  12. 2024Neural Active Learning Beyond Banditscited by 10
8 more
  1. 2024Meta Clustering of Neural Banditscited by 7
  2. 2025Invariant Link Selector for Spatial-Temporal Out-of-Distribution Problemcited by 5
  3. 2024Influence Maximization via Graph Neural Banditscited by 4
  4. 2025Online Clustering of Dueling Banditscited by 3
  5. 2023Leveraging heterogeneous spillover in maximizing contextual bandit rewardscited by 2
  6. 2024Graph Neural Thompson Samplingcited by 1
  7. 2025Learning Peer Influence Probabilities with Linear Contextual Banditscited by 1
  8. 2026The Bandit's Blind Spot: The Critical Role of User State Representation in Recommender Systemscited by 0