{"url":"/method/fastgcn","slug":"fastgcn","name":"FastGCN","full_name":"FastGCN","full_name_withheld":false,"description_markdown":"FastGCN is a fast improvement of the GCN model recently proposed by Kipf & Welling (2016a) for learning graph embeddings. It generalizes transductive training to an inductive manner and also addresses the memory bottleneck issue of GCN caused by recursive expansion of neighborhoods. The crucial ingredient is a sampling scheme in the reformulation of the loss and the gradient, well justified through an alternative view of graph convoluntions in the form of integral transforms of embedding functions.\r\n\r\nDescription and image from: [FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling](https://arxiv.org/pdf/1801.10247.pdf)","description_state":"present","introduced_year":null,"introduced_by":{"title":null,"paper":null,"first_author":null,"n_authors":0,"url_abs":null,"archive_paper_url":null},"source":{"url":"http://arxiv.org/abs/1801.10247v1","title":"FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling","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":5,"archive_num_papers":null,"papers_newest_first":[{"paper":null,"title":"A Local Graph Limits Perspective on Sampling-Based GNNs","date":"2023-10-17","arxiv_id":"2310.10953","n_code_links":0,"syntology":null},{"paper":null,"title":"On Batch-size Selection for Stochastic Training for Graph Neural Networks","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"title":"Urban Traffic Flow Forecast Based on FastGCRNN","date":"2020-09-17","arxiv_id":"2009.08087","n_code_links":0,"syntology":null},{"paper":"/paper/simplifying-graph-convolutional-networks","title":"Simplifying Graph Convolutional Networks","date":"2019-02-19","arxiv_id":"1902.07153","n_code_links":7,"syntology":{"ran":3,"of":8,"unverified":5,"pointer_only":0}},{"paper":"/paper/fastgcn-fast-learning-with-graph","title":"FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling","date":"2018-01-30","arxiv_id":"1801.10247","n_code_links":4,"syntology":{"ran":0,"of":3,"unverified":3,"pointer_only":3}}],"papers_shown":5,"tasks":[{"task":"/task/node-classification","name":"Node Classification","papers":3},{"task":"/task/decoder","name":"Decoder","papers":1},{"task":"/task/deep-learning","name":"Deep Learning","papers":1},{"task":"/task/graph-regression","name":"Graph Regression","papers":1},{"task":"/task/image-classification","name":"Image Classification","papers":1},{"task":"/task/node-classification-on-non-homophilic","name":"Node Classification on Non-Homophilic (Heterophilic) Graphs","papers":1},{"task":"/task/relation-extraction","name":"Relation Extraction","papers":1},{"task":"/task/sentiment-analysis","name":"Sentiment Analysis","papers":1},{"task":"/task/skeleton-based-action-recognition","name":"Skeleton Based Action Recognition","papers":1},{"task":"/task/text-classification","name":"Text Classification","papers":1}],"tasks_shown":10,"n_tasks":10,"usage_by_year":[{"year":"2018","papers":1},{"year":"2019","papers":1},{"year":"2020","papers":1},{"year":"2021","papers":1},{"year":"2023","papers":1}],"row_source":"embedded","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/fastgcn"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}