{"url":"/method/cluster-gcn","slug":"cluster-gcn","name":"Cluster-GCN","full_name":"Cluster-GCN","full_name_withheld":false,"description_markdown":"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.\r\n\r\nDescription and image from: [Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks](https://arxiv.org/pdf/1905.07953.pdf)","description_state":"present","introduced_year":null,"introduced_by":{"title":"Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks","paper":"/paper/cluster-gcn-an-efficient-algorithm-for","first_author":"Wei-Lin Chiang","n_authors":6,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/cluster-gcn-an-efficient-algorithm-for"},"source":{"url":"https://arxiv.org/abs/1905.07953v2","title":"Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks","url_on_a_paper_host":true},"code_snippet_url":null,"code_snippet_url_on_a_code_host":false,"categories":[{"area":"Graphs","area_id":"graphs","collection":"Graph Models","url":"/methods/category/graph-models","pwc_aliases":[]}],"n_papers_tagged":3,"archive_num_papers":3,"papers_newest_first":[{"paper":"/paper/a-new-graph-node-classification-benchmark","title":"A New Graph Node Classification Benchmark: Learning Structure from Histology Cell Graphs","date":"2022-11-11","arxiv_id":"2211.06292","n_code_links":1,"syntology":{"ran":0,"of":3,"unverified":3,"pointer_only":0}},{"paper":"/paper/gcn-based-linkage-prediction-for-face","title":"A Linkage-based Doubly Imbalanced Graph Learning Framework for Face Clustering","date":"2021-07-06","arxiv_id":"2107.02477","n_code_links":1,"syntology":null},{"paper":"/paper/cluster-gcn-an-efficient-algorithm-for","title":"Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks","date":"2019-05-20","arxiv_id":"1905.07953","n_code_links":6,"syntology":null}],"papers_shown":3,"tasks":[{"task":"/task/clustering","name":"Clustering","papers":2},{"task":"/task/graph-learning","name":"Graph Learning","papers":2},{"task":"/task/node-classification","name":"Node Classification","papers":2},{"task":"/task/computational-efficiency","name":"Computational Efficiency","papers":1},{"task":"/task/face-clustering","name":"Face Clustering","papers":1},{"task":"/task/graph-clustering","name":"Graph Clustering","papers":1},{"task":"/task/graph-neural-network","name":"Graph Neural Network","papers":1},{"task":"/task/link-prediction","name":"Link Prediction","papers":1},{"task":"/task/node-property-prediction","name":"Node Property Prediction","papers":1},{"task":"/task/image-classification","name":"image-classification","papers":1},{"task":"/task/imbalanced-classification","name":"imbalanced classification","papers":1},{"task":"/task/whole-slide-images","name":"whole slide images","papers":1}],"tasks_shown":12,"n_tasks":12,"usage_by_year":[{"year":"2019","papers":1},{"year":"2021","papers":1},{"year":"2022","papers":1}],"row_source":"methods_table","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/cluster-gcn"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}