mlsanityData to 7 Oct 2026

Triple Sparsification of Graph Convolutional Networks without Sacrificing the Accuracy

arXiv 2208.03559 ↗First posted 6 Aug 2022cs.LG cs.PFcited by 4

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Abstract (arXiv)

Graph Neural Networks (GNNs) are widely used to perform different machine learning tasks on graphs. As the size of the graphs grows, and the GNNs get deeper, training and inference time become costly in addition to the memory requirement. Thus, without sacrificing accuracy, graph sparsification, or model compression becomes a viable approach for graph learning tasks. A few existing techniques only study the sparsification of graphs and GNN models. In this paper, we develop a SparseGCN pipeline to study all possible sparsification in GNN. We provide a theoretical analysis and empirically show that it can add up to 11.6% additional sparsity to the embedding matrix without sacrificing the accuracy of the commonly used benchmark graph datasets.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 25 of its 33 references that are in the map. First posted on arXiv 6 Aug 2022.

Builds on

25 of 33 references.

References · 25

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  3. 2017Graph Attention Networkscited by 8,854
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13 more
  1. 2019Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networkscited by 540
  2. 2018Deep Learning on Graphs: A Surveycited by 393
  3. 2021Graph Self-Supervised Learning: A Surveycited by 289
  4. 2020Sparse GPU Kernels for Deep Learningcited by 138
  5. 2021A Unified Lottery Ticket Hypothesis for Graph Neural Networkscited by 118
  6. 2020Degree-Quant: Quantization-Aware Training for Graph Neural Networkscited by 93
  7. 2019Sparse Graph Attention Networkscited by 41
  8. 2020Binary Graph Neural Networkscited by 32
  9. 2020Bi-GCN: Binary Graph Convolutional Networkcited by 27
  10. 2020Force2Vec: Parallel force-directed graph embeddingcited by 7
  11. 2020Bandwidth-Optimized Parallel Algorithms for Sparse Matrix-Matrix Multiplication using Propagation Blockingcited by 3
  12. 2021A Comprehensive Analytical Survey on Unsupervised and Semi-Supervised Graph Representation Learning Methodscited by 2
  13. 2022MarkovGNN: Graph Neural Networks on Markov Diffusioncited by 1

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Cited by year

Citing papers per year: 2023: 2; 2024: 1; 2025: 1.

4 papers.

  1. 2024EXGC: Bridging Efficiency and Explainability in Graph Condensationcited by 20
  2. 2025Recent Advances in Malware Detection: Graph Learning and Explainabilitycited by 6
  3. 2023Exploiting On-chip Heterogeneity of Versal Architecture for GNN Inference Accelerationcited by 2
  4. 2023Graph Neural Network for Accurate and Low-complexity SAR ATRcited by 1