mlsanityData to 14 Oct 2026

GCondenser: Benchmarking Graph Condensation

arXiv 2405.14246 ↗First posted 23 May 2024cs.LGcited by 5

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Abstract (arXiv)

Large-scale graphs are valuable for graph representation learning, yet the abundant data in these graphs hinders the efficiency of the training process. Graph condensation (GC) alleviates this issue by compressing the large graph into a significantly smaller one that still supports effective model training. Although recent research has introduced various approaches to improve the effectiveness of the condensed graph, comprehensive and practical evaluations across different GC methods are neglected. This paper proposes the first large-scale graph condensation benchmark, GCondenser, to holistically evaluate and compare mainstream GC methods. GCondenser includes a standardised GC paradigm, consisting of condensation, validation, and evaluation procedures, as well as enabling extensions to new GC methods and datasets. With GCondenser, a comprehensive performance study is conducted, presenting the effectiveness of existing methods. GCondenser is open-sourced and available at https://github.com/superallen13/GCondenser.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 30 of its 42 references that are in the map. First posted on arXiv 23 May 2024.

Builds on

30 of 42 references.

References · 30

  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. 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,163
  5. 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,089
  6. 2019Simplifying Graph Convolutional Networkscited by 1,477
  7. 2018Predict then Propagate: Graph Neural Networks meet Personalized PageRankcited by 993
  8. 2020Dataset Condensation with Gradient Matchingcited by 516
  9. 2022Dataset Distillation by Matching Training Trajectoriescited by 442
  10. 2021Dataset Condensation with Distribution Matchingcited by 365
  11. 2021Dataset Condensation with Differentiable Siamese Augmentationcited by 291
  12. 2022Rethinking Graph Neural Networks for Anomaly Detectioncited by 187
18 more
  1. 2020Design Space for Graph Neural Networkscited by 182
  2. 2019Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernelscited by 161
  3. 2021Graph Condensation for Graph Neural Networkscited by 150
  4. 2022Condensing Graphs via One-Step Gradient Matchingcited by 100
  5. 2023Structure-free Graph Condensation: From Large-scale Graphs to Condensed Graph-free Datacited by 71
  6. 2022DC-BENCH: Dataset Condensation Benchmarkcited by 67
  7. 2022Graph Condensation via Receptive Field Distribution Matchingcited by 60
  8. 2024A Comprehensive Survey on Graph Reduction: Sparsification, Coarsening, and Condensationcited by 57
  9. 2023CaT: Balanced Continual Graph Learning with Graph Condensationcited by 52
  10. 2023Does Graph Distillation See Like Vision Dataset Counterpart?cited by 45
  11. 2023Fast Graph Condensation with Structure-based Neural Tangent Kernelcited by 36
  12. 2023Graph Condensation for Inductive Node Representation Learningcited by 31
  13. 2024Navigating Complexity: Toward Lossless Graph Condensation via Expanding Window Matchingcited by 28
  14. 2024Rethinking and Accelerating Graph Condensation: A Training-Free Approach with Class Partitioncited by 23
  15. 2022Infinite Recommendation Networks: A Data-Centric Approachcited by 20
  16. 2020COPT: Coordinated Optimal Transport for Graph Sketchingcited by 17
  17. 2023PUMA: Efficient Continual Graph Learning for Node Classification with Graph Condensationcited by 9
  18. 2023FedGKD: Unleashing the Power of Collaboration in Federated Graph Neural Networkscited by 7

Built on by

Cited by year (2026 to 14 Oct)

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

5 papers.

  1. 2024GC-Bench: An Open and Unified Benchmark for Graph Condensationcited by 12
  2. 2024GC4NC: A Benchmark Framework for Graph Condensation on Node Classification with New Insightscited by 7
  3. 2024Contrastive Graph Condensation: Advancing Data Versatility through Self-Supervised Learningcited by 6
  4. 2025Efficient and Privacy-Preserved Link Prediction via Condensed Graphscited by 0
  5. 2026Graph Reduction in Multirelational Networks: A Spreading-Oriented Reduction Benchmarkcited by 0