Methods › Graphs › Graph Models › Cluster-GCN
Cluster-GCN
Introduced by Wei-Lin Chiang et al. in Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Cluster-GCN is a novel GCN algorithm that is suitable for SGD-based training by exploiting the graph clustering structure. Cluster-GCN works as the following: at each step, it samples a block of nodes that associate with a dense subgraph identified by a graph clustering algorithm, and restricts the neighborhood search within this subgraph. This simple but effective strategy leads to significantly improved memory and computational efficiency while being able to achieve comparable test accuracy with previous algorithms.
Description and image from: Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks
Papers archive 2025-07-28
3 shown of 3, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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A New Graph Node Classification Benchmark: Learning Structure from Histology Cell Graphs 11 Nov 2022 · 1 repository · arXiv:2211.06292Syntology ran 0 of 3 samples · 3 unverified
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A Linkage-based Doubly Imbalanced Graph Learning Framework for Face Clustering 6 Jul 2021 · 1 repository · arXiv:2107.02477
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Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks 20 May 2019 · 6 repositories · arXiv:1905.07953
Tasks archive 2025-07-28
12 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
Usage over time archive 2025-07-28
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Categories archive 2025-07-28
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