mlsanityData to 7 Oct 2026

Node Identifiers: Compact, Discrete Representations for Efficient Graph Learning

arXiv 2405.16435 ↗First posted 26 May 2024cs.LGcited by 10

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Abstract (arXiv)

We present a novel end-to-end framework that generates highly compact (typically 6-15 dimensions), discrete (int4 type), and interpretable node representations, termed node identifiers (node IDs), to tackle inference challenges on large-scale graphs. By employing vector quantization, we compress continuous node embeddings from multiple layers of a Graph Neural Network (GNN) into discrete codes, applicable under both self-supervised and supervised learning paradigms. These node IDs capture high-level abstractions of graph data and offer interpretability that traditional GNN embeddings lack. Extensive experiments on 34 datasets, encompassing node classification, graph classification, link prediction, and attributed graph clustering tasks, demonstrate that the generated node IDs significantly enhance speed and memory efficiency while achieving competitive performance compared to current state-of-the-art methods.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 110 of its 142 references that are in the map. First posted on arXiv 26 May 2024.

Builds on

110 of 142 references.

References · 110

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Graph Attention Networkscited by 8,854
  3. 2017Inductive Representation Learning on Large Graphscited by 6,284
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38 more
  1. 2016Variational Graph Auto-Encoderscited by 1,373
  2. 2020Graph Contrastive Learning with Augmentationscited by 1,203
  3. 2018Deep Graph Infomaxcited by 1,101
  4. 2018Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learningcited by 1,099
  5. 2013Spectral Networks and Locally Connected Networks on Graphscited by 1,097
  6. 2017MoleculeNet: A Benchmark for Molecular Machine Learningcited by 1,037
  7. 2018Predict then Propagate: Graph Neural Networks meet Personalized PageRankcited by 987
  8. 2016Revisiting Semi-Supervised Learning with Graph Embeddingscited by 970
  9. 2019Strategies for Pre-training Graph Neural Networkscited by 964
  10. 2018Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networkscited by 878
  11. 2020Simple and Deep Graph Convolutional Networkscited by 857
  12. 2018Link Prediction Based on Graph Neural Networkscited by 829
  13. 2020Geom-GCN: Geometric Graph Convolutional Networkscited by 821
  14. 2018Hierarchical Graph Representation Learning with Differentiable Poolingcited by 786
  15. 2020TUDataset: A collection of benchmark datasets for learning with graphscited by 718
  16. 2019Graph Neural Networks for Social Recommendationcited by 711
  17. 2022Autoregressive Image Generation using Residual Quantizationcited by 706
  18. 2022Recipe for a General, Powerful, Scalable Graph Transformercited by 702
  19. 2020Self-supervised Learning: Generative or Contrastivecited by 693
  20. 2020A Generalization of Transformer Networks to Graphscited by 653
  21. 2020Contrastive Multi-View Representation Learning on Graphscited by 651
  22. 2020Adaptive Universal Generalized PageRank Graph Neural Networkcited by 592
  23. 2019Multi-scale Attributed Node Embeddingcited by 543
  24. 2020Jukebox: A Generative Model for Musiccited by 535
  25. 2019DeepGCNs: Can GCNs Go as Deep as CNNs?cited by 527
  26. 2022DiGress: Discrete Denoising diffusion for graph generationcited by 503
  27. 2021Rethinking Graph Transformers with Spectral Attentioncited by 468
  28. 2022GraphMAE: Self-Supervised Masked Graph Autoencoderscited by 443
  29. 2019InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximizationcited by 441
  30. 2023Finite Scalar Quantization: VQ-VAE Made Simplecited by 425
  31. 2023A critical look at the evaluation of GNNs under heterophily: Are we really making progress?cited by 332
  32. 2021Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methodscited by 309
  33. 2021Graph Contrastive Learning Automatedcited by 287
  34. 2021Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Predictioncited by 273
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  36. 2022Long Range Graph Benchmarkcited by 258
  37. 2020Wiki-CS: A Wikipedia-Based Benchmark for Graph Neural Networkscited by 253
  38. 2023GraphGPT: Graph Instruction Tuning for Large Language Modelscited by 223

50 most cited of 110 All 110 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 6; 2026: 3.

10 papers.

  1. 2024Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classificationcited by 83
  2. 2025Can Classic GNNs Be Strong Baselines for Graph-level Tasks? Simple Architectures Meet Excellencecited by 24
  3. 2025Discrete Tokenization for Multimodal LLMs: A Comprehensive Surveycited by 7
  4. 2025Graph is a Natural Regularization: Revisiting Vector Quantization for Graph Representation Learningcited by 2
  5. 2025How Can Mamba Learn In Context with Outliers and Generalize Provably?cited by 2
  6. 2025Theoretical Learning Performance of Graph Neural Networks: The Impact of Jumping Connections and Layer-wise Sparsificationcited by 0
  7. 2025Two Facets of the Same Optimization Coin: Model Degradation and Representation Collapse in Graph Foundation Modelscited by 0
  8. 2026Multi-scale Graph Autoregressive Modeling: Molecular Property Prediction via Next Token Predictioncited by 0
  9. 2026CGRL: Causal-Guided Representation Learning for Node-Level Out-of-Distribution Generalizationcited by 0
  10. 2026When Machines Speak: A Unified Generative Framework for Integrating Machine-Native Symbols into Pretrained Large Language Modelscited by 0