{"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/a-comprehensive-study-on-large-scale-graph","title":"A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking","arxiv_id":"2210.07494","date":"2022-10-14","proceeding":null,"authors":["Keyu Duan","Zirui Liu","Peihao Wang","Wenqing Zheng","Kaixiong Zhou","Tianlong Chen","Xia Hu","Zhangyang Wang"],"abstract":"Large-scale graph training is a notoriously challenging problem for graph neural networks (GNNs). Due to the nature of evolving graph structures into the training process, vanilla GNNs usually fail to scale up, limited by the GPU memory space. Up to now, though numerous scalable GNN architectures have been proposed, we still lack a comprehensive survey and fair benchmark of this reservoir to find the rationale for designing scalable GNNs. To this end, we first systematically formulate the representative methods of large-scale graph training into several branches and further establish a fair and consistent benchmark for them by a greedy hyperparameter searching. In addition, regarding efficiency, we theoretically evaluate the time and space complexity of various branches and empirically compare them w.r.t GPU memory usage, throughput, and convergence. Furthermore, We analyze the pros and cons for various branches of scalable GNNs and then present a new ensembling training manner, named EnGCN, to address the existing issues. Our code is available at https://github.com/VITA-Group/Large_Scale_GCN_Benchmarking.","url_abs":"https://arxiv.org/abs/2210.07494v2","url_pdf":"https://arxiv.org/pdf/2210.07494v2.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":"a-comprehensive-study-on-large-scale-graph","repo_url":"https://github.com/vita-group/large_scale_gcn_benchmarking","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"a-comprehensive-study-on-large-scale-graph","repo_url":"https://github.com/VITA-Group/Large_Scale_GCN_Benchmarking","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"benchmarking","task_name":"Benchmarking"},{"task_slug":null,"task_name":"GPU"},{"task_slug":"node-classification","task_name":"Node Classification"},{"task_slug":"node-property-prediction","task_name":"Node Property Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/node-classification-on-flickr","task":"Node Classification","dataset":"Flickr","model":"EnGCN (Duan et al., 2022)","rank_in_archive_order":4,"of":8,"metrics":{"Accuracy":"0.562"},"uses_additional_data":false},{"leaderboard":"/sota/node-classification-on-reddit","task":"Node Classification","dataset":"Reddit","model":"EnGCN","rank_in_archive_order":7,"of":16,"metrics":{"Accuracy":"96.65%"},"uses_additional_data":false},{"leaderboard":"/sota/node-property-prediction-on-ogbn-products","task":"Node Property Prediction","dataset":"ogbn-products","model":"EnGCN","rank_in_archive_order":2,"of":64,"metrics":{"Ext. data":"No","Number of params":"653918","Test Accuracy":"0.8798 ± 0.0004","Validation Accuracy":"0.9241 ± 0.0003"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2210.07494","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.07494"}},"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. 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