mlsanityData to 7 Oct 2026

Towards Data-centric Graph Machine Learning: Review and Outlook

arXiv 2309.10979 ↗First posted 20 Sep 2023cs.LGcited by 15

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Abstract (arXiv)

Data-centric AI, with its primary focus on the collection, management, and utilization of data to drive AI models and applications, has attracted increasing attention in recent years. In this article, we conduct an in-depth and comprehensive review, offering a forward-looking outlook on the current efforts in data-centric AI pertaining to graph data-the fundamental data structure for representing and capturing intricate dependencies among massive and diverse real-life entities. We introduce a systematic framework, Data-centric Graph Machine Learning (DC-GML), that encompasses all stages of the graph data lifecycle, including graph data collection, exploration, improvement, exploitation, and maintenance. A thorough taxonomy of each stage is presented to answer three critical graph-centric questions: (1) how to enhance graph data availability and quality; (2) how to learn from graph data with limited-availability and low-quality; (3) how to build graph MLOps systems from the graph data-centric view. Lastly, we pinpoint the future prospects of the DC-GML domain, providing insights to navigate its advancements and applications.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 132 of its 223 references that are in the map. First posted on arXiv 20 Sep 2023.

Builds on

132 of 223 references.

References · 132

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38 more
  1. 2020Graph Contrastive Learning with Augmentationscited by 1,203
  2. 2018Deep Graph Infomaxcited by 1,101
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Citing papers per year: 2023: 6; 2024: 8; 2025: 1.

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  1. 2023Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoningcited by 326
  2. 2024A Comprehensive Survey on Graph Reduction: Sparsification, Coarsening, and Condensationcited by 57
  3. 2024ARC: A Generalist Graph Anomaly Detector with In-Context Learningcited by 46
  4. 2023Towards Self-Interpretable Graph-Level Anomaly Detectioncited by 41
  5. 2023PREM: A Simple Yet Effective Approach for Node-Level Graph Anomaly Detectioncited by 25
  6. 2024Graph Condensation: A Surveycited by 23
  7. 2023Data-centric Graph Learning: A Surveycited by 22
  8. 2023GNNEvaluator: Evaluating GNN Performance On Unseen Graphs Without Labelscited by 17
  9. 2024Graph Learning under Distribution Shifts: A Comprehensive Survey on Domain Adaptation, Out-of-distribution, and Continual Learningcited by 17
  10. 2024Online GNN Evaluation Under Test-time Graph Distribution Shiftscited by 12
  11. 2023Data Optimization in Deep Learning: A Surveycited by 6
  12. 2024WelQrate: Defining the Gold Standard in Small Molecule Drug Discovery Benchmarkingcited by 5
3 more
  1. 2024Towards Data-centric Machine Learning on Directed Graphs: a Surveycited by 2
  2. 2024Acceleration Algorithms in GNNs: A Surveycited by 0
  3. 2025Toward Data-centric Directed Graph Learning: An Entropy-driven Approachcited by 0