{"paper":{"title":"Identification of core-periphery structure in networks","license":"http://creativecommons.org/licenses/by/3.0/","headline":"","cross_cats":["cond-mat.stat-mech","physics.soc-ph"],"primary_cat":"cs.SI","authors_text":"M. E. J. Newman, Travis Martin, Xiao Zhang","submitted_at":"2014-09-16T21:44:49Z","abstract_excerpt":"Many networks can be usefully decomposed into a dense core plus an outlying, loosely-connected periphery. Here we propose an algorithm for   performing such a decomposition on empirical network data using methods   of statistical inference. Our method fits a generative model of   core-periphery structure to observed data using a combination of an   expectation--maximization algorithm for calculating the parameters of the   model and a belief propagation algorithm for calculating the   decomposition itself. We find the method to be efficient, scaling easily   to networks with a million or more "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1409.4813","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}