{"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/fast-maximum-likelihood-estimation-via","title":"Fast Maximum Likelihood estimation via Equilibrium Expectation for Large Network Data","arxiv_id":"1802.10311","date":"2018-02-28","proceeding":null,"authors":["Maksym Byshkin","Alex Stivala","Antonietta Mira","Garry Robins","Alessandro Lomi"],"abstract":"A major line of contemporary research on complex networks is based on the\ndevelopment of statistical models that specify the local motifs associated with\nmacro-structural properties observed in actual networks. This statistical\napproach becomes increasingly problematic as network size increases. In the\ncontext of current research on efficient estimation of models for large network\ndata sets, we propose a fast algorithm for maximum likelihood estimation (MLE)\nthat afords a signifcant increase in the size of networks amenable to direct\nempirical analysis. The algorithm we propose in this paper relies on properties\nof Markov chains at equilibrium, and for this reason it is called equilibrium\nexpectation (EE). We demonstrate the performance of the EE algorithm in the\ncontext of exponential random graphmodels (ERGMs) a family of statistical\nmodels commonly used in empirical research based on network data observed at a\nsingle period in time. Thus far, the lack of efcient computational strategies\nhas limited the empirical scope of ERGMs to relatively small networks with a\nfew thousand nodes. The approach we propose allows a dramatic increase in the\nsize of networks that may be analyzed using ERGMs. This is illustrated in an\nanalysis of several biological networks and one social network with 104,103\nnodes","url_abs":"http://arxiv.org/abs/1802.10311v2","url_pdf":"http://arxiv.org/pdf/1802.10311v2.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":"fast-maximum-likelihood-estimation-via","repo_url":"https://github.com/Byshkin/EquilibriumExpectation","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"fast-maximum-likelihood-estimation-via","repo_url":"https://github.com/stivalaa/EstimNetDirected","is_official":0,"mentioned_in_paper":0,"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}