{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T07:21:42Z","timestamp":1791444102379,"version":"4.3.3"},"reference-count":67,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2023,1,27]],"date-time":"2023-01-27T00:00:00Z","timestamp":1674777600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,2,27]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>In a complex network, the core component with interesting structures is usually hidden within noninformative connections. The noises and bias introduced by the noninformative component can obscure the salient structure and limit many network modeling procedures\u2019 effectiveness. This paper introduces a novel core\u2013periphery model for the noninformative periphery structure of networks without imposing a specific form of the core. We propose spectral algorithms for core identification for general downstream network analysis tasks under the model. The algorithms enjoy strong performance guarantees and are scalable for large networks. We evaluate the methods by extensive simulation studies demonstrating advantages over multiple traditional core\u2013periphery methods. The methods are also used to extract the core structure from a citation network, which results in a more interpretable hierarchical community detection.<\/jats:p>","DOI":"10.1093\/jrsssb\/qkac009","type":"journal-article","created":{"date-parts":[[2023,1,28]],"date-time":"2023-01-28T09:01:58Z","timestamp":1674896518000},"page":"108-126","source":"Crossref","is-referenced-by-count":4,"title":["Informative core identification in complex networks"],"prefix":"10.1093","volume":"85","author":[{"given":"Ruizhong","family":"Miao","sequence":"first","affiliation":[{"name":"Department of Statistics, University of Virginia , Charlottesville, VA , 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