{"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/graph-learning-under-sparsity-priors","title":"Graph learning under sparsity priors","arxiv_id":"1707.05587","date":"2017-07-18","proceeding":null,"authors":["Hermina Petric Maretic","Dorina Thanou","Pascal Frossard"],"abstract":"Graph signals offer a very generic and natural representation for data that\nlives on networks or irregular structures. The actual data structure is however\noften unknown a priori but can sometimes be estimated from the knowledge of the\napplication domain. If this is not possible, the data structure has to be\ninferred from the mere signal observations. This is exactly the problem that we\naddress in this paper, under the assumption that the graph signals can be\nrepresented as a sparse linear combination of a few atoms of a structured graph\ndictionary. The dictionary is constructed on polynomials of the graph\nLaplacian, which can sparsely represent a general class of graph signals\ncomposed of localized patterns on the graph. We formulate a graph learning\nproblem, whose solution provides an ideal fit between the signal observations\nand the sparse graph signal model. As the problem is non-convex, we propose to\nsolve it by alternating between a signal sparse coding and a graph update step.\nWe provide experimental results that outline the good graph recovery\nperformance of our method, which generally compares favourably to other recent\nnetwork inference algorithms.","url_abs":"http://arxiv.org/abs/1707.05587v1","url_pdf":"http://arxiv.org/pdf/1707.05587v1.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":"graph-learning-under-sparsity-priors","repo_url":"https://github.com/Hermina/GraphLearningSparsityPriors","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"graph-learning","task_name":"Graph Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}