{"id":13564855,"url":"https://github.com/matenure/FastGCN","last_synced_at":"2025-04-03T21:32:03.391Z","repository":{"id":37335837,"uuid":"119597123","full_name":"matenure/FastGCN","owner":"matenure","description":"The sample codes for our ICLR18 paper \"FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling\"\"","archived":false,"fork":false,"pushed_at":"2021-03-25T03:16:19.000Z","size":5196,"stargazers_count":519,"open_issues_count":25,"forks_count":111,"subscribers_count":12,"default_branch":"master","last_synced_at":"2024-11-04T18:45:16.609Z","etag":null,"topics":["fastgcn","graph-convolutional-networks","graphsage","reddit"],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/matenure.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2018-01-30T21:33:04.000Z","updated_at":"2024-10-30T07:45:13.000Z","dependencies_parsed_at":"2022-08-18T03:45:55.038Z","dependency_job_id":null,"html_url":"https://github.com/matenure/FastGCN","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/matenure%2FFastGCN","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/matenure%2FFastGCN/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/matenure%2FFastGCN/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/matenure%2FFastGCN/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/matenure","download_url":"https://codeload.github.com/matenure/FastGCN/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247083920,"owners_count":20880941,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["fastgcn","graph-convolutional-networks","graphsage","reddit"],"created_at":"2024-08-01T13:01:37.066Z","updated_at":"2025-04-03T21:32:01.357Z","avatar_url":"https://github.com/matenure.png","language":"Python","funding_links":[],"categories":["Python","Uncategorized"],"sub_categories":["Uncategorized"],"readme":"# FastGCN\nThis is the Tensorflow implementation of our ICLR2018 paper: [\"**FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling**\".](https://openreview.net/forum?id=rytstxWAW\u0026noteId=ByU9EpGSf)\n\n\nInstructions of the sample codes:\n\n[For Reddit dataset]\n\n\ttrain_batch_multiRank_inductive_reddit_Mixlayers_sampleA.py is the final model. (precomputated the AH in the bottom layer) The original Reddit data should be transferred into the .npz format using this function: transferRedditDataFormat.\n\tNote: By default, this code does no sampling. To enable sampling, change `main(None)` at the bottom to `main(100)`. (The number is the sample size. You can also try other sample sizes)\n\n\ttrain_batch_multiRank_inductive_reddit_Mixlayers_uniform.py is the model for uniform sampling.\n\n\ttrain_batch_multiRank_inductive_reddit_Mixlayers_appr2layers.py is the model for 2-layer approximation.\n\n\tcreate_Graph_forGraphSAGE.py is used to transfer the data into the GraphSAGE format, so that users can compare our method with GraphSAGE. We also include the transferred original Cora dataset in this repository (./data/cora_graphSAGE).\n\n\n[For pubmed or cora]\n\n\ttrain.py is the original GCN model.\n\n \tpubmed_Mix_sampleA.py \tThe dataset could be defined in the codes, for example: flags.DEFINE_string('dataset', 'pubmed', 'Dataset string.')\n\n\tpubmed_Mix_uniform.py and pubmed_inductive_appr2layers.py are similar to the ones for reddit.\n\n\tpubmed-original**.py means the codes are used for original Cora or Pubmed datasets. Users could also change their datasets by changing the data load function from load_data() to load_data_original().\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmatenure%2FFastGCN","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmatenure%2FFastGCN","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmatenure%2FFastGCN/lists"}