{"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/source-localization-on-graphs-via-l1-recovery","title":"Source Localization on Graphs via l1 Recovery and Spectral Graph Theory","arxiv_id":"1603.07584","date":"2016-03-24","proceeding":null,"authors":["Rodrigo Pena","Xavier Bresson","Pierre Vandergheynst"],"abstract":"We cast the problem of source localization on graphs as the simultaneous\nproblem of sparse recovery and diffusion kernel learning. An l1 regularization\nterm enforces the sparsity constraint while we recover the sources of diffusion\nfrom a single snapshot of the diffusion process. The diffusion kernel is\nestimated by assuming the process to be as generic as the standard heat\ndiffusion. We show with synthetic data that we can concomitantly learn the\ndiffusion kernel and the sources, given an estimated initialization. We\nvalidate our model with cholera mortality and atmospheric tracer diffusion\ndata, showing also that the accuracy of the solution depends on the\nconstruction of the graph from the data points.","url_abs":"http://arxiv.org/abs/1603.07584v2","url_pdf":"http://arxiv.org/pdf/1603.07584v2.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":"source-localization-on-graphs-via-l1-recovery","repo_url":"https://github.com/rodrigo-pena/src-localization-graphs","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","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}