mlsanityData to 7 Oct 2026

Graph Coarsening via Convolution Matching for Scalable Graph Neural Network Training

arXiv 2312.15520 ↗First posted 24 Dec 2023cs.LGcited by 12

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Abstract (arXiv)

Graph summarization as a preprocessing step is an effective and complementary technique for scalable graph neural network (GNN) training. In this work, we propose the Coarsening Via Convolution Matching (CONVMATCH) algorithm and a highly scalable variant, A-CONVMATCH, for creating summarized graphs that preserve the output of graph convolution. We evaluate CONVMATCH on six real-world link prediction and node classification graph datasets, and show it is efficient and preserves prediction performance while significantly reducing the graph size. Notably, CONVMATCH achieves up to 95% of the prediction performance of GNNs on node classification while trained on graphs summarized down to 1% the size of the original graph. Furthermore, on link prediction tasks, CONVMATCH consistently outperforms all baselines, achieving up to a 2x improvement.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 27 of its 47 references that are in the map. First posted on arXiv 24 Dec 2023.

Builds on

27 of 47 references.

References · 27

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,414
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15 more
  1. 2020DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphscited by 139
  2. 2019Soft-Label Dataset Distillation and Text Dataset Distillationcited by 106
  3. 2022Condensing Graphs via One-Step Gradient Matchingcited by 100
  4. 2020Flexible Dataset Distillation: Learn Labels Instead of Imagescited by 91
  5. 2021Scaling Up Graph Neural Networks Via Graph Coarseningcited by 80
  6. 2022Graph Condensation via Receptive Field Distribution Matchingcited by 60
  7. 2019GraphZoom: A multi-level spectral approach for accurate and scalable graph embeddingcited by 58
  8. 2018MILE: A Multi-Level Framework for Scalable Graph Embeddingcited by 28
  9. 2021Learn Locally, Correct Globally: A Distributed Algorithm for Training Graph Neural Networkscited by 24
  10. 2016Graph Summarization Methods and Applications: A Surveycited by 15
  11. 2020Faster Graph Embeddings via Coarseningcited by 15
  12. 2021Communication-Efficient Sampling for Distributed Training of Graph Convolutional Networkscited by 7
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 3; 2026: 5.

12 papers.

  1. 2024Rethinking and Accelerating Graph Condensation: A Training-Free Approach with Class Partitioncited by 23
  2. 2024A Survey on Graph Condensationcited by 9
  3. 2024Graph Coarsening with Message-Passing Guaranteescited by 9
  4. 2025Taxonomy of reduction matrices for Graph Coarseningcited by 3
  5. 2025Partition-wise Graph Filtering: A Unified Perspective Through the Lens of Graph Coarseningcited by 2
  6. 2024FIT-GNN: Faster Inference Time for GNNs that 'FIT' in Memory Using Coarseningcited by 1
  7. 2026Rethinking Efficient Graph Coarsening via a Non-Selfishness Principlecited by 1
  8. 2025Adaptive Graph Coarsening for Efficient GNN Trainingcited by 0
  9. 2026Improving LLM Reasoning with Homophily-aware Structural and Semantic Text-Attributed Graph Compressioncited by 0
  10. 2026Scalable Topology-Preserving Graph Coarsening: Concepts and Algorithmscited by 0
  11. 2026Efficient and Scalable Granular-ball Graph Coarsening Method for Large-scale Graph Node Classificationcited by 0
  12. 2026CoRe-GNN: Multilevel Message passing on Coarsened graphscited by 0