mlsanityData to 14 Oct 2026

Graph Attention Multi-Layer Perceptron

arXiv 2206.04355 ↗First posted 9 Jun 2022cs.LG cs.AIcited by 83

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Abstract (arXiv)

Graph neural networks (GNNs) have achieved great success in many graph-based applications. However, the enormous size and high sparsity level of graphs hinder their applications under industrial scenarios. Although some scalable GNNs are proposed for large-scale graphs, they adopt a fixed K-hop neighborhood for each node, thus facing the over-smoothing issue when adopting large propagation depths for nodes within sparse regions. To tackle the above issue, we propose a new GNN architecture -- Graph Attention Multi-Layer Perceptron (GAMLP), which can capture the underlying correlations between different scales of graph knowledge. We have deployed GAMLP in Tencent with the Angel platform, and we further evaluate GAMLP on both real-world datasets and large-scale industrial datasets. Extensive experiments on these 14 graph datasets demonstrate that GAMLP achieves state-of-the-art performance while enjoying high scalability and efficiency. Specifically, it outperforms GAT by 1.3% regarding predictive accuracy on our large-scale Tencent Video dataset while achieving up to 50× training speedup. Besides, it ranks top-1 on both the leaderboards of the largest homogeneous and heterogeneous graph (i.e., ogbn-papers100M and ogbn-mag) of Open Graph Benchmark.

Key facts. Cited by 83 papers in the map (data through 14 Oct 2026). Builds on 46 of its 63 references that are in the map. First posted on arXiv 9 Jun 2022.

Builds on

46 of 63 references.

References · 46

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2017Graph Attention Networkscited by 8,923
  3. 2017Inductive Representation Learning on Large Graphscited by 6,320
  4. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,434
  5. 2017Modeling Relational Data with Graph Convolutional Networkscited by 1,694
  6. 2019Simplifying Graph Convolutional Networkscited by 1,477
  7. 2018Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learningcited by 1,102
  8. 2018Pitfalls of Graph Neural Network Evaluationcited by 1,007
  9. 2018Predict then Propagate: Graph Neural Networks meet Personalized PageRankcited by 993
  10. 2018Representation Learning on Graphs with Jumping Knowledge Networkscited by 877
  11. 2020Simple and Deep Graph Convolutional Networkscited by 860
  12. 2019Graph Neural Networks for Social Recommendationcited by 714
34 more
  1. 2019GraphSAINT: Graph Sampling Based Inductive Learning Methodcited by 618
  2. 2018FastGCN: Fast Learning with Graph Convolutional Networks via Importance Samplingcited by 584
  3. 2020Heterogeneous Graph Transformercited by 582
  4. 2019Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networkscited by 541
  5. 2019DeepGCNs: Can GCNs Go as Deep as CNNs?cited by 527
  6. 2020Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classificationcited by 506
  7. 2021OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphscited by 329
  8. 2020GPT-GNN: Generative Pre-Training of Graph Neural Networkscited by 303
  9. 2020SIGN: Scalable Inception Graph Neural Networkscited by 284
  10. 2020DeeperGCN: All You Need to Train Deeper GCNscited by 250
  11. 2020Combining Label Propagation and Simple Models Out-performs Graph Neural Networkscited by 202
  12. 2018Adaptive Sampling Towards Fast Graph Representation Learningcited by 180
  13. 2019AliGraph: A Comprehensive Graph Neural Network Platformcited by 153
  14. 2020Structural Deep Clustering Networkcited by 145
  15. 2020DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphscited by 139
  16. 2020When Does Self-Supervision Help Graph Convolutional Networks?cited by 121
  17. 2019Multi-Stage Self-Supervised Learning for Graph Convolutional Networks on Graphs with Few Labelscited by 111
  18. 2020Adaptive Graph Encoder for Attributed Graph Embeddingcited by 89
  19. 2020Scalable Graph Neural Networks via Bidirectional Propagationcited by 87
  20. 2017Knowledge Graph Completion via Complex Tensor Factorizationcited by 80
  21. 2021Extract the Knowledge of Graph Neural Networks and Go Beyond it: An Effective Knowledge Distillation Frameworkcited by 69
  22. 2021OpenBox: A Generalized Black-box Optimization Servicecited by 46
  23. 2021Node Dependent Local Smoothing for Scalable Graph Learningcited by 45
  24. 2021Bag of Tricks for Node Classification with Graph Neural Networkscited by 40
  25. 2022PaSca: a Graph Neural Architecture Search System under the Scalable Paradigmcited by 37
  26. 2020Adaptive Propagation Graph Convolutional Networkcited by 31
  27. 2020Scalable Graph Neural Networks for Heterogeneous Graphscited by 31
  28. 2021Heterogeneous Graph Representation Learning with Relation Awarenesscited by 31
  29. 2020Graph-Based Neural Network Models with Multiple Self-Supervised Auxiliary Taskscited by 21
  30. 2021Evaluating Deep Graph Neural Networkscited by 19
  31. 2021ROD: Reception-aware Online Distillation for Sparse Graphscited by 18
  32. 2020Hybrid Micro/Macro Level Convolution for Heterogeneous Graph Learningcited by 9
  33. 2021R-GSN: The Relation-based Graph Similar Network for Heterogeneous Graphcited by 6
  34. 2022ZOOMER: Boosting Retrieval on Web-scale Graphs by Regions of Interestcited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 12; 2023: 24; 2024: 22; 2025: 15; 2026: 10.

83 papers.

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  1. 2022Learning on Large-scale Text-attributed Graphs via Variational Inferencecited by 155
  2. 2023GraphText: Graph Reasoning in Text Spacecited by 111
  3. 2024OpenGraph: Towards Open Graph Foundation Modelscited by 78
  4. 2022Simple and Efficient Heterogeneous Graph Neural Networkcited by 71
  5. 2023Structure-free Graph Condensation: From Large-scale Graphs to Condensed Graph-free Datacited by 71
  6. 2023GADBench: Revisiting and Benchmarking Supervised Graph Anomaly Detectioncited by 58
  7. 2022A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinkingcited by 56
  8. 2022Certified Graph Unlearningcited by 46
  9. 2024Graph Transformers: A Surveycited by 32
  10. 2022MLPInit: Embarrassingly Simple GNN Training Acceleration with MLP Initializationcited by 31
  11. 2023Graph Condensation for Inductive Node Representation Learningcited by 31
  12. 2024Linear-Time Graph Neural Networks for Scalable Recommendationscited by 31
38 more
  1. 2023Learning Strong Graph Neural Networks with Weak Informationcited by 25
  2. 2024AdaFGL: A New Paradigm for Federated Node Classification with Topology Heterogeneitycited by 25
  3. 2024Can We Remove the Square-Root in Adaptive Gradient Methods? A Second-Order Perspectivecited by 25
  4. 2024Graph Condensation: A Surveycited by 23
  5. 2023Breaking the Entanglement of Homophily and Heterophily in Semi-supervised Node Classificationcited by 19
  6. 2023Hierarchical Transformer for Scalable Graph Learningcited by 17
  7. 2023LazyGNN: Large-Scale Graph Neural Networks via Lazy Propagationcited by 16
  8. 2023Efficient Heterogeneous Graph Learning via Random Projectioncited by 16
  9. 2024OpenFGL: A Comprehensive Benchmark for Federated Graph Learningcited by 16
  10. 2023Boosting Multitask Learning on Graphs through Higher-Order Task Affinitiescited by 15
  11. 2024Topology-aware Embedding Memory for Continual Learning on Expanding Networkscited by 14
  12. 2025Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approachcited by 14
  13. 2023Differentially Private Decoupled Graph Convolutions for Multigranular Topology Protectioncited by 12
  14. 2024Modeling Inter-Intra Heterogeneity for Graph Federated Learningcited by 12
  15. 2022NAFS: A Simple yet Tough-to-beat Baseline for Graph Representation Learningcited by 11
  16. 2024LightDiC: A Simple yet Effective Approach for Large-scale Digraph Representation Learningcited by 11
  17. 2024Noise-Resilient Unsupervised Graph Representation Learning via Multi-Hop Feature Quality Estimationcited by 11
  18. 2023Semantic-aware Node Synthesis for Imbalanced Heterogeneous Information Networkscited by 9
  19. 2024GTC: GNN-Transformer Co-contrastive Learning for Self-supervised Heterogeneous Graph Representationcited by 9
  20. 2023Spectral Heterogeneous Graph Convolutions via Positive Noncommutative Polynomialscited by 8
  21. 2023Accelerating Scalable Graph Neural Network Inference with Node-Adaptive Propagationcited by 8
  22. 2024GraphAgent: Agentic Graph Language Assistantcited by 8
  23. 2022Efficient Graph Neural Network Inference at Large Scalecited by 7
  24. 2023Label Deconvolution for Node Representation Learning on Large-scale Attributed Graphs against Learning Biascited by 7
  25. 2025DiffGraph: Heterogeneous Graph Diffusion Modelcited by 6
  26. 2025Toward General and Robust LLM-enhanced Text-attributed Graph Learningcited by 6
  27. 2022Clenshaw Graph Neural Networkscited by 5
  28. 2023Causal-Based Supervision of Attention in Graph Neural Network: A Better and Simpler Choice towards Powerful Attentioncited by 5
  29. 2023Efficient End-to-end Language Model Fine-tuning on Graphscited by 5
  30. 2025View Space: Learning Representation across Arbitrary Graphscited by 5
  31. 2022Intrinsic Dimension for Large-Scale Geometric Learningcited by 4
  32. 2023Long-range Meta-path Search on Large-scale Heterogeneous Graphscited by 4
  33. 2026Fixed Aggregation Features Can Rival GNNscited by 4
  34. 2023Seq-HGNN: Learning Sequential Node Representation on Heterogeneous Graphcited by 3
  35. 2024Unifews: You Need Fewer Operations for Efficient Graph Neural Networkscited by 2
  36. 2024Efficient Graph Optimization via Distance-Aware Graph Representation Learningcited by 2
  37. 2024Towards Data-centric Machine Learning on Directed Graphs: a Surveycited by 2
  38. 2025ScaDyG:A New Paradigm for Large-scale Dynamic Graph Learningcited by 2

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