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The model is based on representing the graph as an exchangeable point process and naturally generalizes existing probabilistic models with overlapping block structure to the sparse regime. Our construction builds on vectors of completely random measures and has interpretable parameters, each node being assigned a vector representing its levels of affiliation to some latent communities. We develop methods for efficient simulation of this class of random graphs and for scalable posterior inference. We show that the approach proposed can recover interpretable structure of real world networks and can handle graphs with thousands of nodes and tens of thousands of edges.<\/jats:p>","DOI":"10.1111\/rssb.12363","type":"journal-article","created":{"date-parts":[[2020,3,10]],"date-time":"2020-03-10T04:45:16Z","timestamp":1583815516000},"page":"487-520","source":"Crossref","is-referenced-by-count":24,"title":["Exchangeable Random Measures for Sparse and Modular Graphs with Overlapping Communities"],"prefix":"10.1093","volume":"82","author":[{"given":"Adrien","family":"Todeschini","sequence":"first","affiliation":[{"name":"Institut National de Recherche en Informatique et en Automatique and Institut de Math\u00e9matiques de Bordeaux \u00a0 France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xenia","family":"Miscouridou","sequence":"additional","affiliation":[{"name":"University of Oxford \u00a0 UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fran\u00e7ois","family":"Caron","sequence":"additional","affiliation":[{"name":"University of Oxford \u00a0 UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2020,3,9]]},"reference":[{"key":"2023022408363527800_","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1145\/1134271.1134277","volume-title":"Proc. 3rd Int. 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