{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/calibrate-and-debias-layer-wise-sampling-for","title":"Calibrate and Debias Layer-wise Sampling for Graph Convolutional Networks","arxiv_id":"2206.00583","date":"2022-06-01","proceeding":null,"authors":["Yifan Chen","Tianning Xu","Dilek Hakkani-Tur","Di Jin","Yun Yang","Ruoqing Zhu"],"abstract":"Multiple sampling-based methods have been developed for approximating and accelerating node embedding aggregation in graph convolutional networks (GCNs) training. Among them, a layer-wise approach recursively performs importance sampling to select neighbors jointly for existing nodes in each layer. This paper revisits the approach from a matrix approximation perspective, and identifies two issues in the existing layer-wise sampling methods: suboptimal sampling probabilities and estimation biases induced by sampling without replacement. To address these issues, we accordingly propose two remedies: a new principle for constructing sampling probabilities and an efficient debiasing algorithm. The improvements are demonstrated by extensive analyses of estimation variance and experiments on common benchmarks. Code and algorithm implementations are publicly available at https://github.com/ychen-stat-ml/GCN-layer-wise-sampling .","url_abs":"https://arxiv.org/abs/2206.00583v2","url_pdf":"https://arxiv.org/pdf/2206.00583v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"calibrate-and-debias-layer-wise-sampling-for","repo_url":"https://github.com/ychen-stat-ml/gcn-layer-wise-sampling","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2206.00583","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.00583"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/ychen-stat-ml/gcn-layer-wise-sampling","reach":null}],"summary":{"ran_draft_wrong":1,"ran_honours":1,"unverified":4},"by_repo_kind":{"official":{"samples":5,"ran":1,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":1,"samples":[{"code_sha256_prefix":"643e0b76db2096e9","entry":"load_data_graphsage","repo":"ychen-stat-ml/gcn-layer-wise-sampling","repo_kind":"official","path":"sketch/utils_new.py","file_url":"https://github.com/ychen-stat-ml/gcn-layer-wise-sampling/blob/HEAD/sketch/utils_new.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"643e0b76db2096e9"}},{"code_sha256_prefix":"c1d6392f89c5e495","entry":"parse_index_file","repo":null,"repo_kind":null,"path":null,"file_url":null,"link_basis":"identical_code_first_harvested_elsewhere","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"mcp_get_code":{"code_sha256":"c1d6392f89c5e495"}},{"code_sha256_prefix":"0671c5cd513a2c2b","entry":"fastgcn_sampler","repo":"ychen-stat-ml/gcn-layer-wise-sampling","repo_kind":"official","path":"sketch/samplers.py","file_url":"https://github.com/ychen-stat-ml/gcn-layer-wise-sampling/blob/HEAD/sketch/samplers.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"0671c5cd513a2c2b"}},{"code_sha256_prefix":"260cca862d49474d","entry":"fastgcn_sampler_custom","repo":"ychen-stat-ml/gcn-layer-wise-sampling","repo_kind":"official","path":"sketch/samplers.py","file_url":"https://github.com/ychen-stat-ml/gcn-layer-wise-sampling/blob/HEAD/sketch/samplers.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"260cca862d49474d"}},{"code_sha256_prefix":"28a9028cace2e0ac","entry":"ladies_sampler","repo":"ychen-stat-ml/gcn-layer-wise-sampling","repo_kind":"official","path":"sketch/samplers.py","file_url":"https://github.com/ychen-stat-ml/gcn-layer-wise-sampling/blob/HEAD/sketch/samplers.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"28a9028cace2e0ac"}},{"code_sha256_prefix":"be0d0a2bfb17216b","entry":"run_random_walks","repo":"ychen-stat-ml/gcn-layer-wise-sampling","repo_kind":"official","path":"sketch/utils_new.py","file_url":"https://github.com/ychen-stat-ml/gcn-layer-wise-sampling/blob/HEAD/sketch/utils_new.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"be0d0a2bfb17216b"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}