Fast maximum likelihood estimation via equilibrium expectation for large network data
dc.contributor.author | Byshkin, M | |
dc.contributor.author | Stivala, A | |
dc.contributor.author | Mira, A | |
dc.contributor.author | Robins, G | |
dc.contributor.author | Lomi, A | |
dc.date.accessioned | 2019-03-15T10:16:13Z | |
dc.date.issued | 2018-07-31 | |
dc.description.abstract | A major line of contemporary research on complex networks is based on the development of statistical models that specify the local motifs associated with macro-structural properties observed in actual networks. This statistical approach becomes increasingly problematic as network size increases. In the context of current research on efficient estimation of models for large network data sets, we propose a fast algorithm for maximum likelihood estimation (MLE) that affords a significant increase in the size of networks amenable to direct empirical analysis. The algorithm we propose in this paper relies on properties of Markov chains at equilibrium, and for this reason it is called equilibrium expectation (EE). We demonstrate the performance of the EE algorithm in the context of exponential random graph models (ERGMs) a family of statistical models commonly used in empirical research based on network data observed at a single period in time. Thus far, the lack of efficient computational strategies has limited the empirical scope of ERGMs to relatively small networks with a few thousand nodes. The approach we propose allows a dramatic increase in the size of networks that may be analyzed using ERGMs. This is illustrated in an analysis of several biological networks and one social network with 104,103 nodes. | en_GB |
dc.description.sponsorship | Swiss National Science Foundation | en_GB |
dc.identifier.citation | Vol. 8, Article number: 11509 (2018) | en_GB |
dc.identifier.doi | 10.1038/s41598-018-29725-8 | |
dc.identifier.grantnumber | 167362 | en_GB |
dc.identifier.grantnumber | 105218_163196 | en_GB |
dc.identifier.uri | http://hdl.handle.net/10871/36483 | |
dc.language.iso | en | en_GB |
dc.publisher | Nature Research | en_GB |
dc.rights | Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Te images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. © The Author(s) 2018 | en_GB |
dc.subject | Complex networks | en_GB |
dc.subject | Data mining | en_GB |
dc.subject | Mathematics and computing | en_GB |
dc.title | Fast maximum likelihood estimation via equilibrium expectation for large network data | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2019-03-15T10:16:13Z | |
dc.identifier.issn | 2045-2322 | |
dc.description | This is the final version. Available from the publisher via the DOI in this record. | en_GB |
dc.identifier.journal | Scientific Reports | en_GB |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | en_GB |
dcterms.dateAccepted | 2018-07-17 | |
rioxxterms.version | VoR | en_GB |
rioxxterms.licenseref.startdate | 2018-12-01 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2019-03-15T10:10:24Z | |
refterms.versionFCD | VoR | |
refterms.dateFOA | 2019-03-15T10:16:15Z | |
refterms.panel | C | en_GB |
refterms.depositException | publishedGoldOA | |
refterms.depositExceptionExplanation | https://doi.org/10.1038/s41598-018-29725-8 |
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Except where otherwise noted, this item's licence is described as Open Access This article is licensed under a Creative Commons Attribution 4.0 International
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format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Te images or other third party material in this
article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the
material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the
copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
© The Author(s) 2018