{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,26]],"date-time":"2026-05-26T16:04:42Z","timestamp":1779811482730,"version":"3.53.1"},"reference-count":31,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T00:00:00Z","timestamp":1774051200000},"content-version":"vor","delay-in-days":79,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["DMS-2015492"],"award-info":[{"award-number":["DMS-2015492"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["DMS-2310764"],"award-info":[{"award-number":["DMS-2310764"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,2,13]]},"abstract":"<jats:title>Summary<\/jats:title>\n                  <jats:p>We consider a time-ordered sequence of networks stemming from stochastic block models in which nodes gradually change their membership over time, and no network at any single time-point contains sufficient signal strength to recover its community structure. To estimate the time-varying community structure, we develop the kernel-debiased sum of squares method that performs spectral clustering after a debiased sum-of-squared aggregation of adjacency matrices. Our theory demonstrates, via a novel bias-variance decomposition, that our new method achieves consistent community detection in each network, even for heterophilic networks, without requiring smoothness in the time-varying dynamics of between-community connectivities. We also prove the identifiability of aligning community structures across time based on how rapidly nodes change communities, and we develop a data-adaptive bandwidth-tuning procedure for our new method. We demonstrate the utility and advantages of our proposed method through simulations and a novel analysis of time-varying dynamics in gene coordination in the human developing brain.<\/jats:p>","DOI":"10.1093\/biomet\/asag018","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T12:52:41Z","timestamp":1773838361000},"source":"Crossref","is-referenced-by-count":0,"title":["Dynamic clustering for heterophilic stochastic block models with time-varying node memberships"],"prefix":"10.1093","volume":"113","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1236-9847","authenticated-orcid":false,"given":"K Z","family":"Lin","sequence":"first","affiliation":[{"name":"Department of Biostatistics, University of Washington , Box 351617 , Seattle, Washington 98195,","place":["U.S.A"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3104-9387","authenticated-orcid":false,"given":"J","family":"Lei","sequence":"additional","affiliation":[{"name":"Department of Statistics & Data Science, Carnegie Mellon University , 132 Baker Hall , Pittsburgh, Pennsylvania 15213, \u00a0 jinglei@andrew.cmu.edu","place":["U.S.A."]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,3,21]]},"reference":[{"key":"2026052611263748600_asag018-B1","first-page":"6446","article-title":"Community detection and stochastic block models: recent developments","volume":"18","author":"Abbe","year":"2017","journal-title":"J. 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