Exponential random graph model parameter estimation for very large directed networks
dc.contributor.author | Stivala, A | |
dc.contributor.author | Robins, G | |
dc.contributor.author | Lomi, A | |
dc.date.accessioned | 2020-06-15T13:24:44Z | |
dc.date.issued | 2020-01-24 | |
dc.description.abstract | Exponential random graph models (ERGMs) are widely used for modeling social networks observed at one point in time. However the computational difficulty of ERGM parameter estimation has limited the practical application of this class of models to relatively small networks, up to a few thousand nodes at most, with usually only a few hundred nodes or fewer. In the case of undirected networks, snowball sampling can be used to find ERGM parameter estimates of larger networks via network samples, and recently published improvements in ERGM network distribution sampling and ERGM estimation algorithms have allowed ERGM parameter estimates of undirected networks with over one hundred thousand nodes to be made. However the implementations of these algorithms to date have been limited in their scalability, and also restricted to undirected networks. Here we describe an implementation of the recently published Equilibrium Expectation (EE) algorithm for ERGM parameter estimation of large directed networks. We test it on some simulated networks, and demonstrate its application to an online social network with over 1.6 million nodes. | en_GB |
dc.description.sponsorship | Swiss National Science Foundation | en_GB |
dc.identifier.citation | Vol. 15 (1), article e0227804 | en_GB |
dc.identifier.doi | 10.1371/journal.pone.0227804 | |
dc.identifier.grantnumber | 167326 | en_GB |
dc.identifier.uri | http://hdl.handle.net/10871/121441 | |
dc.language.iso | en | en_GB |
dc.publisher | Public Library of Science | en_GB |
dc.rights | © 2020 Stivala et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. | en_GB |
dc.title | Exponential random graph model parameter estimation for very large directed networks | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2020-06-15T13:24:44Z | |
dc.description | This is the final version. Available on open access from the Public Library of Science via the DOI in this record | en_GB |
dc.description | Data Availability: The data for the Pokec online social network used as the empirical example is publicly available from the Stanford Large Network Dataset Collection (http://snap.stanford.edu/data/soc-Pokec.html). All source code and scripts are publicly available on GitHub at (https://github.com/stivalaa/EstimNetDirected). | en_GB |
dc.identifier.eissn | 1932-6203 | |
dc.identifier.journal | PLoS ONE | en_GB |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en_GB |
dcterms.dateAccepted | 2019-12-31 | |
rioxxterms.version | VoR | en_GB |
rioxxterms.licenseref.startdate | 2020-01-24 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2020-06-15T13:21:58Z | |
refterms.versionFCD | VoR | |
refterms.dateFOA | 2020-06-15T13:24:51Z | |
refterms.panel | C | en_GB |
refterms.depositException | publishedGoldOA |
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Except where otherwise noted, this item's licence is described as © 2020 Stivala et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.