{"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/grand-scalable-graph-random-neural-networks","title":"GRAND+: Scalable Graph Random Neural Networks","arxiv_id":"2203.06389","date":"2022-03-12","proceeding":null,"authors":["Wenzheng Feng","Yuxiao Dong","Tinglin Huang","Ziqi Yin","Xu Cheng","Evgeny Kharlamov","Jie Tang"],"abstract":"Graph neural networks (GNNs) have been widely adopted for semi-supervised learning on graphs. A recent study shows that the graph random neural network (GRAND) model can generate state-of-the-art performance for this problem. However, it is difficult for GRAND to handle large-scale graphs since its effectiveness relies on computationally expensive data augmentation procedures. In this work, we present a scalable and high-performance GNN framework GRAND+ for semi-supervised graph learning. To address the above issue, we develop a generalized forward push (GFPush) algorithm in GRAND+ to pre-compute a general propagation matrix and employ it to perform graph data augmentation in a mini-batch manner. We show that both the low time and space complexities of GFPush enable GRAND+ to efficiently scale to large graphs. Furthermore, we introduce a confidence-aware consistency loss into the model optimization of GRAND+, facilitating GRAND+'s generalization superiority. We conduct extensive experiments on seven public datasets of different sizes. The results demonstrate that GRAND+ 1) is able to scale to large graphs and costs less running time than existing scalable GNNs, and 2) can offer consistent accuracy improvements over both full-batch and scalable GNNs across all datasets.","url_abs":"https://arxiv.org/abs/2203.06389v1","url_pdf":"https://arxiv.org/pdf/2203.06389v1.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":"grand-scalable-graph-random-neural-networks","repo_url":"https://github.com/wzfhaha/grand-plus","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"graph-learning","task_name":"Graph Learning"},{"task_slug":"model-optimization","task_name":"Model Optimization"},{"task_slug":"node-classification","task_name":"Node Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/node-classification-on-mag-scholar-c","task":"Node Classification","dataset":"MAG-scholar-C","model":"FastGCN","rank_in_archive_order":1,"of":4,"metrics":{"Accuracy":"64.3"},"uses_additional_data":false},{"leaderboard":"/sota/node-classification-on-mag-scholar-c","task":"Node Classification","dataset":"MAG-scholar-C","model":"PPRGo","rank_in_archive_order":2,"of":4,"metrics":{"Accuracy":"72.9"},"uses_additional_data":false},{"leaderboard":"/sota/node-classification-on-mag-scholar-c","task":"Node Classification","dataset":"MAG-scholar-C","model":"GraphSAINT","rank_in_archive_order":3,"of":4,"metrics":{"Accuracy":"75.0"},"uses_additional_data":false},{"leaderboard":"/sota/node-classification-on-mag-scholar-c","task":"Node Classification","dataset":"MAG-scholar-C","model":"GRAND+","rank_in_archive_order":4,"of":4,"metrics":{"Accuracy":"80.0"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2203.06389","atlas_url":"https://app.syntology.ai/?focus=2203.06389","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}