mlsanityData to 14 Oct 2026

Data-centric Graph Learning: A Survey

arXiv 2310.04987 ↗First posted 8 Oct 2023cs.LG cs.SIcited by 22

Not on the map. Why?

Abstract (arXiv)

The history of artificial intelligence (AI) has witnessed the significant impact of high-quality data on various deep learning models, such as ImageNet for AlexNet and ResNet. Recently, instead of designing more complex neural architectures as model-centric approaches, the attention of AI community has shifted to data-centric ones, which focuses on better processing data to strengthen the ability of neural models. Graph learning, which operates on ubiquitous topological data, also plays an important role in the era of deep learning. In this survey, we comprehensively review graph learning approaches from the data-centric perspective, and aim to answer three crucial questions: (1) when to modify graph data, (2) what part of the graph data needs modification to unlock the potential of various graph models, and (3) how to safeguard graph models from problematic data influence. Accordingly, we propose a novel taxonomy based on the stages in the graph learning pipeline, and highlight the processing methods for different data structures in the graph data, i.e., topology, feature and label. Furthermore, we analyze some potential problems embedded in graph data and discuss how to solve them in a data-centric manner. Finally, we provide some promising future directions for data-centric graph learning.

Key facts. Cited by 22 papers in the map (data through 14 Oct 2026). Builds on 209 of its 319 references that are in the map. First posted on arXiv 8 Oct 2023.

Builds on

209 of 319 references.

References · 209

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2023GPT-4 Technical Reportcited by 18,396
  3. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  4. 2017Graph Attention Networkscited by 8,923
  5. 2017Inductive Representation Learning on Large Graphscited by 6,320
  6. 2018How Powerful are Graph Neural Networks?cited by 4,393
  7. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,375
  8. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,919
  9. 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,163
  10. 2016node2vec: Scalable Feature Learning for Networkscited by 1,918
  11. 2014DeepWalk: Online Learning of Social Representationscited by 1,547
  12. 2016Variational Graph Auto-Encoderscited by 1,382
38 more
  1. 2019Certified Adversarial Robustness via Randomized Smoothingcited by 1,340
  2. 2020Graph Contrastive Learning with Augmentationscited by 1,208
  3. 2018Graph Convolutional Neural Networks for Web-Scale Recommender Systemscited by 1,138
  4. 2018Deep Graph Infomaxcited by 1,106
  5. 2018Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learningcited by 1,102
  6. 2016Geometric deep learning: going beyond Euclidean datacited by 1,048
  7. 2020Geom-GCN: Geometric Graph Convolutional Networkscited by 826
  8. 2017Deep Bayesian Active Learning with Image Datacited by 819
  9. 2019DropEdge: Towards Deep Graph Convolutional Networks on Node Classificationcited by 680
  10. 2020A Generalization of Transformer Networks to Graphscited by 661
  11. 2020Contrastive Multi-View Representation Learning on Graphscited by 652
  12. 2019GraphSAINT: Graph Sampling Based Inductive Learning Methodcited by 618
  13. 2019Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Boundscited by 610
  14. 2018FastGCN: Fast Learning with Graph Convolutional Networks via Importance Samplingcited by 584
  15. 2020Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networkscited by 566
  16. 2019Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological Viewcited by 545
  17. 2019Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networkscited by 541
  18. 2020Deep Graph Contrastive Representation Learningcited by 539
  19. 2020Temporal Graph Networks for Deep Learning on Dynamic Graphscited by 530
  20. 2022DiGress: Discrete Denoising diffusion for graph generationcited by 520
  21. 2020Graph Contrastive Learning with Adaptive Augmentationcited by 519
  22. 2020Dataset Condensation with Gradient Matchingcited by 516
  23. 2019Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learningcited by 510
  24. 2018Hypergraph Neural Networkscited by 488
  25. 2021Rethinking Graph Transformers with Spectral Attentioncited by 473
  26. 2019BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learningcited by 432
  27. 2019Diffusion Improves Graph Learningcited by 414
  28. 2018GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Modelscited by 413
  29. 2020Graph Structure Learning for Robust Graph Neural Networkscited by 371
  30. 2018Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generationcited by 364
  31. 2021Beyond Low-frequency Information in Graph Convolutional Networkscited by 362
  32. 2021Graph Neural Networks with Learnable Structural and Positional Representationscited by 333
  33. 2020GPT-GNN: Generative Pre-Training of Graph Neural Networkscited by 303
  34. 2019Label Propagation for Deep Semi-supervised Learningcited by 289
  35. 2021Graph Contrastive Learning Automatedcited by 287
  36. 2019Variational Adversarial Active Learningcited by 284
  37. 2022Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equationscited by 283
  38. 2015Deep Convolutional Networks on Graph-Structured Datacited by 246

50 most cited of 209 All 209 (JSON)

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 3; 2024: 9; 2025: 6; 2026: 4.

22 papers.

Sort:

By area:

  1. 2024Demystifying Chains, Trees, and Graphs of Thoughtscited by 63
  2. 2023Graph Prompt Learning: A Comprehensive Survey and Beyondcited by 61
  3. 2024Graph Condensation: A Surveycited by 23
  4. 2024Can Large Language Models Improve the Adversarial Robustness of Graph Neural Networks?cited by 23
  5. 2023Better with Less: A Data-Active Perspective on Pre-Training Graph Neural Networkscited by 14
  6. 2024Beyond Redundancy: Information-aware Unsupervised Multiplex Graph Structure Learningcited by 12
  7. 2024Can Modifying Data Address Graph Domain Adaptation?cited by 9
  8. 2025A Comprehensive Data-centric Overview of Federated Graph Learningcited by 7
  9. 2026Toward Graph-Tokenizing Large Language Models with Reconstructive Graph Instruction Tuningcited by 7
  10. 2024Efficient Topology-aware Data Augmentation for High-Degree Graph Neural Networkscited by 6
  11. 2023Complexity-Guided Curriculum Learning for Text Graphscited by 3
  12. 2024Survey and Taxonomy: The Role of Data-Centric AI in Transformer-Based Time Series Forecastingcited by 3
10 more
  1. 2025Enhancing Resilience for IoE: A Perspective of Networking-Level Safeguardcited by 3
  2. 2026Graph Tokenization for Bridging Graphs and Transformerscited by 3
  3. 2024Towards Lightweight Graph Neural Network Search with Curriculum Graph Sparsificationcited by 2
  4. 2024Towards Data-centric Machine Learning on Directed Graphs: a Surveycited by 2
  5. 2026Data-centric Prompt Tuning for Dynamic Graphscited by 2
  6. 2025ImageSet2Text: Describing Sets of Images through Textcited by 0
  7. 2025Toward Data-centric Directed Graph Learning: An Entropy-driven Approachcited by 0
  8. 2025Defining and Benchmarking a Data-Centric Design Space for Brain Graph Constructioncited by 0
  9. 2025Draw a Portrait of Your Graph Data: An Instance-Level Profiling Framework for Graph-Structured Datacited by 0
  10. 2026ZeroGAR: Benchmarking the Adversarial Robustness of Zero-Shot Graph Modelscited by 0