{"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/gaan-gated-attention-networks-for-learning-on","title":"GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs","arxiv_id":"1803.07294","date":"2018-03-20","proceeding":null,"authors":["Jiani Zhang","Xingjian Shi","Junyuan Xie","Hao Ma","Irwin King","Dit-yan Yeung"],"abstract":"We propose a new network architecture, Gated Attention Networks (GaAN), for\nlearning on graphs. Unlike the traditional multi-head attention mechanism,\nwhich equally consumes all attention heads, GaAN uses a convolutional\nsub-network to control each attention head's importance. We demonstrate the\neffectiveness of GaAN on the inductive node classification problem. Moreover,\nwith GaAN as a building block, we construct the Graph Gated Recurrent Unit\n(GGRU) to address the traffic speed forecasting problem. Extensive experiments\non three real-world datasets show that our GaAN framework achieves\nstate-of-the-art results on both tasks.","url_abs":"http://arxiv.org/abs/1803.07294v1","url_pdf":"http://arxiv.org/pdf/1803.07294v1.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":"gaan-gated-attention-networks-for-learning-on","repo_url":"https://github.com/jennyzhang0215/GaAN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mxnet","reach":{"status":"ok"}}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"node-classification","task_name":"Node Classification"}],"methods":[{"method_slug":"attention","method_name":"Attention"},{"method_slug":"gaan","method_name":"GaAN"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"speed","method_name":"SPEED"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[{"slug":"gaan","name":"GaAN","full_name":"Gated Attention Networks"}],"results":[{"leaderboard":"/sota/node-classification-on-ppi","task":"Node Classification","dataset":"PPI","model":"GaAN","rank_in_archive_order":12,"of":24,"metrics":{"F1":"98.7"},"uses_additional_data":false},{"leaderboard":"/sota/node-property-prediction-on-ogbn-arxiv","task":"Node Property Prediction","dataset":"ogbn-arxiv","model":"GaAN","rank_in_archive_order":70,"of":86,"metrics":{"Ext. data":"No","Number of params":"1471506","Test Accuracy":"0.7197 ± 0.0024","Validation Accuracy":"Please tell us"},"uses_additional_data":false},{"leaderboard":"/sota/node-property-prediction-on-ogbn-proteins","task":"Node Property Prediction","dataset":"ogbn-proteins","model":"GaAN","rank_in_archive_order":22,"of":26,"metrics":{"Ext. data":"No","Number of params":"Please tell us","Test ROC-AUC":"0.7803 ± 0.0073","Validation ROC-AUC":"Please tell us"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1803.07294","atlas_url":"https://app.syntology.ai/?focus=1803.07294","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}