mlsanityData to 14 Oct 2026

Graph Condensation: A Survey

arXiv 2401.11720 ↗First posted 22 Jan 2024cs.LG cs.AIcited by 23

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Abstract (arXiv)

The rapid growth of graph data poses significant challenges in storage, transmission, and particularly the training of graph neural networks (GNNs). To address these challenges, graph condensation (GC) has emerged as an innovative solution. GC focuses on synthesizing a compact yet highly representative graph, enabling GNNs trained on it to achieve performance comparable to those trained on the original large graph. The notable efficacy of GC and its broad prospects have garnered significant attention and spurred extensive research. This survey paper provides an up-to-date and systematic overview of GC, organizing existing research into five categories aligned with critical GC evaluation criteria: effectiveness, generalization, efficiency, fairness, and robustness. To facilitate an in-depth and comprehensive understanding of GC, this paper examines various methods under each category and thoroughly discusses two essential components within GC: optimization strategies and condensed graph generation. We also empirically compare and analyze representative GC methods with diverse optimization strategies based on the five proposed GC evaluation criteria. Finally, we explore the applications of GC in various fields, outline the related open-source libraries, and highlight the present challenges and novel insights, with the aim of promoting advancements in future research. The related resources can be found at https://github.com/XYGaoG/Graph-Condensation-Papers.

Key facts. Cited by 23 papers in the map (data through 14 Oct 2026). Builds on 74 of its 123 references that are in the map. First posted on arXiv 22 Jan 2024.

Builds on

74 of 123 references.

References · 74

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2017Graph Attention Networkscited by 8,923
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38 more
  1. 2018FastGCN: Fast Learning with Graph Convolutional Networks via Importance Samplingcited by 584
  2. 2020Dataset Condensation with Gradient Matchingcited by 516
  3. 2020Graph Neural Networks in Recommender Systems: A Surveycited by 494
  4. 2022Dataset Distillation by Matching Training Trajectoriescited by 442
  5. 2020Explainability in Graph Neural Networks: A Taxonomic Surveycited by 384
  6. 2020Graph Structure Learning for Robust Graph Neural Networkscited by 371
  7. 2021Dataset Condensation with Distribution Matchingcited by 365
  8. 2019Representation Learning for Dynamic Graphs: A Surveycited by 265
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  16. 2019Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernelscited by 161
  17. 2021Graph Condensation for Graph Neural Networkscited by 150
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 12; 2025: 5; 2026: 6.

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  1. 2024A Comprehensive Survey on Graph Reduction: Sparsification, Coarsening, and Condensationcited by 57
  2. 2024Rethinking and Accelerating Graph Condensation: A Training-Free Approach with Class Partitioncited by 23
  3. 2024Reliable Node Similarity Matrix Guided Contrastive Graph Clusteringcited by 12
  4. 2025A Comprehensive Survey on Imbalanced Data Learningcited by 10
  5. 2024A Survey on Graph Condensationcited by 9
  6. 2024RobGC: Towards Robust Graph Condensationcited by 8
  7. 2024GC4NC: A Benchmark Framework for Graph Condensation on Node Classification with New Insightscited by 7
  8. 2024Contrastive Graph Condensation: Advancing Data Versatility through Self-Supervised Learningcited by 6
  9. 2024FedVCK: Non-IID Robust and Communication-Efficient Federated Learning via Valuable Condensed Knowledge for Medical Image Analysiscited by 6
  10. 2024Backdoor Graph Condensationcited by 5
  11. 2025Efficient Graph Condensation via Gaussian Processcited by 4
  12. 2024Tackling Data Heterogeneity in Federated Time Series Forecastingcited by 3
11 more
  1. 2026DSBD: Dual-Aligned Structural Basis Distillation for Graph Domain Adaptationcited by 2
  2. 2024Training-free Heterogeneous Graph Condensation via Data Selectioncited by 1
  3. 2025Dynamic Graph Condensationcited by 1
  4. 2026Rethinking Efficient Graph Coarsening via a Non-Selfishness Principlecited by 1
  5. 2026Position: Graph Condensation Needs a Reset -- Move Beyond Full-dataset Training and Model-Dependencecited by 1
  6. 2024Extending Graph Condensation to Multi-Label Datasets: A Benchmark Studycited by 0
  7. 2025Efficient and Privacy-Preserved Link Prediction via Condensed Graphscited by 0
  8. 2025Adaptive Graph Coarsening for Efficient GNN Trainingcited by 0
  9. 2026Anchor-guided Hypergraph Condensation with Dual-level Discriminationcited by 0
  10. 2026Explicit Bounds on the Entropy of Piecewise H"older Graphon Modelscited by 0
  11. 2026Data-Efficient Agentic Graph Domain Adaptation via Reliability-Aware Prototype Learningcited by 0