{"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/stochastic-blockmodel-approximation-of-a-1","title":"Stochastic blockmodel approximation of a graphon: Theory and consistent estimation","arxiv_id":"1311.1731","date":"2013-11-07","proceeding":"NeurIPS 2013 12","authors":["Edoardo M. Airoldi","Thiago B. Costa","Stanley H. Chan"],"abstract":"Non-parametric approaches for analyzing network data based on exchangeable\ngraph models (ExGM) have recently gained interest. The key object that defines\nan ExGM is often referred to as a graphon. This non-parametric perspective on\nnetwork modeling poses challenging questions on how to make inference on the\ngraphon underlying observed network data. In this paper, we propose a\ncomputationally efficient procedure to estimate a graphon from a set of\nobserved networks generated from it. This procedure is based on a stochastic\nblockmodel approximation (SBA) of the graphon. We show that, by approximating\nthe graphon with a stochastic block model, the graphon can be consistently\nestimated, that is, the estimation error vanishes as the size of the graph\napproaches infinity.","url_abs":"http://arxiv.org/abs/1311.1731v2","url_pdf":"http://arxiv.org/pdf/1311.1731v2.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":"stochastic-blockmodel-approximation-of-a-1","repo_url":"https://github.com/airoldilab/SBA","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"stochastic-block-model","task_name":"Stochastic Block Model"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1311.1731","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}