{"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-study-on-graph-structured-recurrent-neural","title":"A Study on Graph-Structured Recurrent Neural Networks and Sparsification with Application to Epidemic Forecasting","arxiv_id":"1902.05113","date":"2019-02-13","proceeding":null,"authors":["Zhijian Li","Xiyang Luo","Bao Wang","Andrea L. Bertozzi","Jack Xin"],"abstract":"We study epidemic forecasting on real-world health data by a graph-structured\nrecurrent neural network (GSRNN). We achieve state-of-the-art forecasting\naccuracy on the benchmark CDC dataset. To improve model efficiency, we sparsify\nthe network weights via transformed-$\\ell_1$ penalty and maintain prediction\naccuracy at the same level with 70% of the network weights being zero.","url_abs":"http://arxiv.org/abs/1902.05113v1","url_pdf":"http://arxiv.org/pdf/1902.05113v1.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-study-on-graph-structured-recurrent-neural","repo_url":"https://github.com/lzj994/srnn_pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"a-study-on-graph-structured-recurrent-neural","repo_url":"https://github.com/lzj994/SRNN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}