{"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/adaptive-graph-convolutional-neural-networks","title":"Adaptive Graph Convolutional Neural Networks","arxiv_id":"1801.03226","date":"2018-01-10","proceeding":null,"authors":["Ruoyu Li","Sheng Wang","Feiyun Zhu","Junzhou Huang"],"abstract":"Graph Convolutional Neural Networks (Graph CNNs) are generalizations of\nclassical CNNs to handle graph data such as molecular data, point could and\nsocial networks. Current filters in graph CNNs are built for fixed and shared\ngraph structure. However, for most real data, the graph structures varies in\nboth size and connectivity. The paper proposes a generalized and flexible graph\nCNN taking data of arbitrary graph structure as input. In that way a\ntask-driven adaptive graph is learned for each graph data while training. To\nefficiently learn the graph, a distance metric learning is proposed. Extensive\nexperiments on nine graph-structured datasets have demonstrated the superior\nperformance improvement on both convergence speed and predictive accuracy.","url_abs":"http://arxiv.org/abs/1801.03226v1","url_pdf":"http://arxiv.org/pdf/1801.03226v1.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":"adaptive-graph-convolutional-neural-networks","repo_url":"https://github.com/codemarsyu/Adaptive-Graph-Convolutional-Network","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"adaptive-graph-convolutional-neural-networks","repo_url":"https://github.com/uta-smile/Adaptive-Graph-Convolutional-Network","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"metric-learning","task_name":"Metric Learning"}],"methods":[{"method_slug":"agcn","method_name":"AGCN"},{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[],"methods_introduced":[{"slug":"agcn","name":"AGCN","full_name":"Adaptive Graph Convolutional Neural Networks"}],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1801.03226","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}