{"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/reconstructing-networks-with-unknown-and","title":"Reconstructing networks with unknown and heterogeneous errors","arxiv_id":"1806.07956","date":"2018-06-09","proceeding":null,"authors":["Tiago P. Peixoto"],"abstract":"The vast majority of network datasets contains errors and omissions, although\nthis is rarely incorporated in traditional network analysis. Recently, an\nincreasing effort has been made to fill this methodological gap by developing\nnetwork reconstruction approaches based on Bayesian inference. These\napproaches, however, rely on assumptions of uniform error rates and on direct\nestimations of the existence of each edge via repeated measurements, something\nthat is currently unavailable for the majority of network data. Here we develop\na Bayesian reconstruction approach that lifts these limitations by not only\nallowing for heterogeneous errors, but also for single edge measurements\nwithout direct error estimates. Our approach works by coupling the inference\napproach with structured generative network models, which enable the\ncorrelations between edges to be used as reliable uncertainty estimates.\nAlthough our approach is general, we focus on the stochastic block model as the\nbasic generative process, from which efficient nonparametric inference can be\nperformed, and yields a principled method to infer hierarchical community\nstructure from noisy data. We demonstrate the efficacy of our approach with a\nvariety of empirical and artificial networks.","url_abs":"http://arxiv.org/abs/1806.07956v3","url_pdf":"http://arxiv.org/pdf/1806.07956v3.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":"reconstructing-networks-with-unknown-and","repo_url":"https://git.skewed.de/count0/graph-tool","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"bayesian-inference","task_name":"Bayesian Inference"},{"task_slug":"stochastic-block-model","task_name":"Stochastic Block Model"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}