mlsanityData to 7 Oct 2026

Linear-Time Graph Neural Networks for Scalable Recommendations

arXiv 2402.13973 ↗First posted 21 Feb 2024cs.IR cs.LGcited by 31

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Abstract (arXiv)

In an era of information explosion, recommender systems are vital tools to deliver personalized recommendations for users. The key of recommender systems is to forecast users' future behaviors based on previous user-item interactions. Due to their strong expressive power of capturing high-order connectivities in user-item interaction data, recent years have witnessed a rising interest in leveraging Graph Neural Networks (GNNs) to boost the prediction performance of recommender systems. Nonetheless, classic Matrix Factorization (MF) and Deep Neural Network (DNN) approaches still play an important role in real-world large-scale recommender systems due to their scalability advantages. Despite the existence of GNN-acceleration solutions, it remains an open question whether GNN-based recommender systems can scale as efficiently as classic MF and DNN methods. In this paper, we propose a Linear-Time Graph Neural Network (LTGNN) to scale up GNN-based recommender systems to achieve comparable scalability as classic MF approaches while maintaining GNNs' powerful expressiveness for superior prediction accuracy. Extensive experiments and ablation studies are presented to validate the effectiveness and scalability of the proposed algorithm. Our implementation based on PyTorch is available.

Key facts. Cited by 31 papers in the map (data through 7 Oct 2026). Builds on 33 of its 54 references that are in the map. First posted on arXiv 21 Feb 2024.

Builds on

33 of 54 references.

References · 33

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  3. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
  4. 2019Simplifying Graph Convolutional Networkscited by 1,470
  5. 2020LightGCN: Simplifying and Powering Graph Convolution Network for Recommendationcited by 1,134
  6. 2018Graph Convolutional Neural Networks for Web-Scale Recommender Systemscited by 1,133
  7. 2017Neural Collaborative Filteringcited by 1,108
  8. 2018Predict then Propagate: Graph Neural Networks meet Personalized PageRankcited by 987
  9. 2019Graph Neural Networks for Social Recommendationcited by 711
  10. 2019Neural Graph Collaborative Filteringcited by 660
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  12. 2019KGAT: Knowledge Graph Attention Network for Recommendationcited by 342
21 more
  1. 2020Self-supervised Graph Learning for Recommendationcited by 305
  2. 2020Combining Label Propagation and Simple Models Out-performs Graph Neural Networkscited by 199
  3. 2019Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networkscited by 177
  4. 2021Are Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendationcited by 173
  5. 2019Implicit Deep Learningcited by 124
  6. 2020Disentangled Graph Collaborative Filteringcited by 118
  7. 2018Billion-scale Commodity Embedding for E-commerce Recommendation in Alibabacited by 105
  8. 2020Implicit Graph Neural Networkscited by 98
  9. 2022Graph Attention Multi-Layer Perceptroncited by 82
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  11. 2020Minimal Variance Sampling with Provable Guarantees for Fast Training of Graph Neural Networkscited by 54
  12. 2021Graph Trend Filtering Networks for Recommendationscited by 53
  13. 2018NAIS: Neural Attentive Item Similarity Model for Recommendationcited by 52
  14. 2023Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generationcited by 42
  15. 2024Macro Graph Neural Networks for Online Billion-Scale Recommender Systemscited by 40
  16. 2023Fast Graph Condensation with Structure-based Neural Tangent Kernelcited by 36
  17. 2019Deep Social Collaborative Filteringcited by 32
  18. 2022GraphFM: Improving Large-Scale GNN Training via Feature Momentumcited by 31
  19. 2023Fairly Adaptive Negative Sampling for Recommendationscited by 22
  20. 2023LazyGNN: Large-Scale Graph Neural Networks via Lazy Propagationcited by 16
  21. 2023Multi-agent Attacks for Black-box Social Recommendationscited by 9

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 10; 2025: 13; 2026: 8.

31 papers.

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  1. 2024A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Modelscited by 450
  2. 2024A Survey of Mambacited by 39
  3. 2024TokenRec: Learning to Tokenize ID for LLM-based Generative Recommendationcited by 35
  4. 2024Graph Machine Learning in the Era of Large Language Models (LLMs)cited by 33
  5. 2024Graph Unlearning with Efficient Partial Retrainingcited by 20
  6. 2025Theoretical Guarantees for High Order Trajectory Refinement in Generative Flowscited by 9
  7. 2025Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transformcited by 9
  8. 2025You Are What You Bought: Generating Customer Personas for E-commerce Applicationscited by 7
  9. 2024Backdoor Graph Condensationcited by 5
  10. 2024SORSA: Singular Values and Orthonormal Regularized Singular Vectors Adaptation of Large Language Modelscited by 5
  11. 2025Unlearning Inversion Attacks for Graph Neural Networkscited by 4
  12. 2025Exposing Privacy Risks in Graph Retrieval-Augmented Generationcited by 4
19 more
  1. 2025Neural Algorithmic Reasoning for Hypergraphs with Looped Transformerscited by 3
  2. 2026Enhancing Molecular Property Predictions by Learning from Bond Modelling and Interactionscited by 3
  3. 2024MGM: Global Understanding of Audience Overlap Graphs for Predicting the Factuality and the Bias of News Mediacited by 2
  4. 2025Theoretical Foundation of Flow-Based Time Series Generation: Provable Approximation, Generalization, and Efficiencycited by 2
  5. 2025Ultra Fast Warm Start Solution for Graph Recommendationscited by 2
  6. 2024Binary Hypothesis Testing for Softmax Models and Leverage Score Modelscited by 1
  7. 2025Enhance GNNs with Reliable Confidence Estimation via Adversarial Calibration Learningcited by 1
  8. 2026Attack by Unlearning: Unlearning-Induced Adversarial Attacks on Graph Neural Networkscited by 1
  9. 2024Your Graph Recommender is Provably a Single-view Graph Contrastive Learningcited by 0
  10. 2025H3GNNs: Harmonizing Heterophily and Homophily in GNNs via Joint Structural Node Encoding and Self-Supervised Learningcited by 0
  11. 2025PBiLoss: Popularity-Aware Regularization to Improve Fairness in Graph-Based Recommender Systemscited by 0
  12. 2025Lighter-X: An Efficient and Plug-and-play Strategy for Graph-based Recommendation through Decoupled Propagationcited by 0
  13. 2025E2E-GRec: An End-to-End Joint Training Framework for Graph Neural Networks and Recommender Systemscited by 0
  14. 2026AdvSynGNN: Structure-Adaptive Graph Neural Nets via Adversarial Synthesis and Self-Corrective Propagationcited by 0
  15. 2026A Multi-View Media Profiling Suite: Resources, Evaluation, and Analysiscited by 0
  16. 2026The Confidence Trap: Calibration Attacks for Graph Neural Networkscited by 0
  17. 2026DrainSinkhorn: Safe Elimination for Batched Entropic Optimal Transportcited by 0
  18. 2026PersonaMem-v3: Toward Omni-Platform Personal Intelligence for Holistic User Understanding, Recommendation, and Agentic Taskscited by 0
  19. 2026ReCoG: Reciprocal Co-Evolution for Multimodal Graph Learningcited by 0