{"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/probability-density-estimation-for-sets-of","title":"Probability density estimation for sets of large graphs with respect to spectral information using stochastic block models","arxiv_id":"2207.02168","date":"2022-07-05","proceeding":null,"authors":["Daniel Ferguson","François G. Meyer"],"abstract":"For graph-valued data sampled iid from a distribution $\\mu$, the sample moments are computed with respect to a choice of metric. In this work, we equip the set of graphs with the pseudo-metric defined by the $\\ell_2$ norm between the eigenvalues of the respective adjacency matrices. We use this pseudo metric and the respective sample moments of a graph valued data set to infer the parameters of a distribution $\\hat{\\mu}$ and interpret this distribution as an approximation of $\\mu$. We verify experimentally that complex distributions $\\mu$ can be approximated well taking this approach.","url_abs":"https://arxiv.org/abs/2207.02168v1","url_pdf":"https://arxiv.org/pdf/2207.02168v1.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":"probability-density-estimation-for-sets-of","repo_url":"https://github.com/zhaokg/rbeast","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"density-estimation","task_name":"Density Estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}