{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T13:20:35Z","timestamp":1768742435489,"version":"3.49.0"},"reference-count":35,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2019,9,17]],"date-time":"2019-09-17T00:00:00Z","timestamp":1568678400000},"content-version":"vor","delay-in-days":365,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/100000001","name":"NSF","doi-asserted-by":"publisher","award":["DMS-1600568 to L.A.B.)"],"award-info":[{"award-number":["DMS-1600568 to L.A.B.)"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000005","name":"DOD","doi-asserted-by":"publisher","award":["HDTRA1-15-0049 to B.Z.W."],"award-info":[{"award-number":["HDTRA1-15-0049 to B.Z.W."]}],"id":[{"id":"10.13039\/100000005","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,6,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>One of the most important features observed in real networks is that, as a network\u2019s topology evolves so does the network\u2019s ability to perform various complex tasks. To explain this, it has also been observed that as a network grows certain subnetworks begin to specialize the function(s) they perform. Herein, we introduce a class of models of network growth based on this notion of specialization and show that as a network is specialized using this method its topology becomes increasingly sparse, modular and hierarchical, each of which are important properties observed in real networks. This procedure is also highly flexible in that a network can be specialized over any subset of its elements. This flexibility allows those studying specific networks the ability to search for mechanisms that describe their growth. For example, we find that by randomly selecting these elements a network\u2019s topology acquires some of the most well-known properties of real networks including the small-world property, disassortativity and a right-skewed degree distribution. Beyond this, we show how this model can be used to generate networks with real-world like clustering coefficients and power-law degree distributions, respectively. As far as the authors know, this is the first such class of models that can create an increasingly modular and hierarchical network topology with these properties.<\/jats:p>","DOI":"10.1093\/comnet\/cny024","type":"journal-article","created":{"date-parts":[[2018,8,31]],"date-time":"2018-08-31T11:28:19Z","timestamp":1535714899000},"page":"375-392","source":"Crossref","is-referenced-by-count":6,"title":["Specialization Models of Network Growth"],"prefix":"10.1093","volume":"7","author":[{"given":"L A","family":"Bunimovich","sequence":"first","affiliation":[{"name":"School of Mathematics, Georgia Institute of Technology, 686 Cherry Street, Atlanta, GA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"D C","family":"Smith","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Brigham Young University, TMCB 274, Provo, UT, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"B Z","family":"Webb","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Brigham Young University, TMCB 274, Provo, UT, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2018,9,17]]},"reference":[{"key":"2020030521255808200_B1","doi-asserted-by":"crossref","first-page":"770","DOI":"10.1038\/nrm2503","article-title":"Modelling and analysis of gene regulatory networks","volume":"9","author":"Karlebach","year":"2008","journal-title":"Nat. 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