mlsanityData to 14 Oct 2026

LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation

arXiv 2002.02126 ↗First posted 6 Feb 2020cs.IR cs.LGcited by 1,139

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Cited by 38 in LLM agents and tool use, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Graph Convolution Network (GCN) has become new state-of-the-art for collaborative filtering. Nevertheless, the reasons of its effectiveness for recommendation are not well understood. Existing work that adapts GCN to recommendation lacks thorough ablation analyses on GCN, which is originally designed for graph classification tasks and equipped with many neural network operations. However, we empirically find that the two most common designs in GCNs -- feature transformation and nonlinear activation -- contribute little to the performance of collaborative filtering. Even worse, including them adds to the difficulty of training and degrades recommendation performance. In this work, we aim to simplify the design of GCN to make it more concise and appropriate for recommendation. We propose a new model named LightGCN, including only the most essential component in GCN -- neighborhood aggregation -- for collaborative filtering. Specifically, LightGCN learns user and item embeddings by linearly propagating them on the user-item interaction graph, and uses the weighted sum of the embeddings learned at all layers as the final embedding. Such simple, linear, and neat model is much easier to implement and train, exhibiting substantial improvements (about 16.0% relative improvement on average) over Neural Graph Collaborative Filtering (NGCF) -- a state-of-the-art GCN-based recommender model -- under exactly the same experimental setting. Further analyses are provided towards the rationality of the simple LightGCN from both analytical and empirical perspectives.

Key facts. Cited by 1,139 papers in the map (data through 14 Oct 2026). First posted on arXiv 6 Feb 2020.

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Citing papers per year: 2021: 92; 2022: 144; 2023: 226; 2024: 256; 2025: 256; 2026: 165.

1,139 papers.

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  1. 2023Large Language Models are Zero-Shot Rankers for Recommender Systemscited by 309
  2. 2023GraphGPT: Graph Instruction Tuning for Large Language Modelscited by 226
  3. 2023Large Language Models on Graphs: A Comprehensive Surveycited by 192
  4. 2023Recommender Systems in the Era of Large Language Models (LLMs)cited by 189
  5. 2023Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approachcited by 168
  6. 2023Representation Learning with Large Language Models for Recommendationcited by 155
  7. 2023How Can Recommender Systems Benefit from Large Language Models: A Surveycited by 142
  8. 2023User Behavior Simulation with Large Language Model based Agentscited by 140
  9. 2023RecMind: Large Language Model Powered Agent For Recommendationcited by 129
  10. 2021Advances and Challenges in Conversational Recommender Systems: A Surveycited by 118
  11. 2023PALR: Personalization Aware LLMs for Recommendationcited by 96
  12. 2021Learning Intents behind Interactions with Knowledge Graph for Recommendationcited by 94
38 more
  1. 2022Intent Contrastive Learning for Sequential Recommendationcited by 91
  2. 2023On Generative Agents in Recommendationcited by 90
  3. 2022Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learningcited by 86
  4. 2023A Survey of Graph Meets Large Language Model: Progress and Future Directionscited by 85
  5. 2022Recommendation Unlearningcited by 80
  6. 2021Mining Latent Structures for Multimedia Recommendationcited by 79
  7. 2024OpenGraph: Towards Open Graph Foundation Modelscited by 78
  8. 2024Natural Language Understanding and Inference with MLLM in Visual Question Answering: A Surveycited by 76
  9. 2021Graph-MLP: Node Classification without Message Passing in Graphcited by 73
  10. 2023LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendationcited by 72
  11. 2023A Comprehensive Survey on Deep Graph Representation Learningcited by 72
  12. 2024A Survey of Large Language Models for Graphscited by 72
  13. 2022Knowledge Graph Contrastive Learning for Recommendationcited by 70
  14. 2021Continuous-Time Sequential Recommendation with Temporal Graph Collaborative Transformercited by 69
  15. 2021AutoDebias: Learning to Debias for Recommendationcited by 68
  16. 2021TCL: Transformer-based Dynamic Graph Modelling via Contrastive Learningcited by 68
  17. 2021SimpleX: A Simple and Strong Baseline for Collaborative Filteringcited by 65
  18. 2022A Survey of Graph Neural Networks for Social Recommender Systemscited by 65
  19. 2024Large Language Model Agent for Hyper-Parameter Optimizationcited by 64
  20. 2022Hypergraph Contrastive Collaborative Filteringcited by 63
  21. 2023GIF: A General Graph Unlearning Strategy via Influence Functioncited by 60
  22. 2021A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendationcited by 59
  23. 2021Graph Learning based Recommender Systems: A Reviewcited by 59
  24. 2025Knowledge Graph Retrieval-Augmented Generation for LLM-based Recommendationcited by 57
  25. 2022A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinkingcited by 56
  26. 2021A Survey of Community Detection Approaches: From Statistical Modeling to Deep Learningcited by 55
  27. 2021Graph Trend Filtering Networks for Recommendationscited by 53
  28. 2022Graph Filters for Signal Processing and Machine Learning on Graphscited by 53
  29. 2023On the steerability of large language models toward data-driven personascited by 53
  30. 2024AnyGraph: Graph Foundation Model in the Wildcited by 53
  31. 2021How Powerful is Graph Convolution for Recommendation?cited by 50
  32. 2024Large Language Models meet Collaborative Filtering: An Efficient All-round LLM-based Recommender Systemcited by 50
  33. 2021Knowledge-aware Coupled Graph Neural Network for Social Recommendationcited by 49
  34. 2025KGGen: Extracting Knowledge Graphs from Plain Text with Language Modelscited by 49
  35. 2021Augmenting Sequential Recommendation with Pseudo-Prior Items via Reversely Pre-training Transformercited by 45
  36. 2021Node Dependent Local Smoothing for Scalable Graph Learningcited by 45
  37. 2022Multi-Behavior Hypergraph-Enhanced Transformer for Sequential Recommendationcited by 42
  38. 2023Towards Understanding the Generalization of Graph Neural Networkscited by 41

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