{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T18:48:17Z","timestamp":1767034097392,"version":"3.37.3"},"reference-count":8,"publisher":"Oxford University Press (OUP)","issue":"18","license":[{"start":{"date-parts":[[2019,2,13]],"date-time":"2019-02-13T00:00:00Z","timestamp":1550016000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/100006129","name":"FCT","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100006129","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Portuguese Foundation for Science and Technology","award":["UID\/EEA50014\/2013"],"award-info":[{"award-number":["UID\/EEA50014\/2013"]}]},{"DOI":"10.13039\/100000181","name":"United States Air Force Office of Scientific Research","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100000181","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000181","name":"AFOSR","doi-asserted-by":"publisher","award":["YIP FA9550-16-1-0147"],"award-info":[{"award-number":["YIP FA9550-16-1-0147"]}],"id":[{"id":"10.13039\/100000181","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,9,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Network alignment (NA) finds conserved regions between two networks. NA methods optimize node conservation (NC) and edge conservation. Dynamic graphlet degree vectors are a state-of-the-art dynamic NC measure, used within the fastest and most accurate NA method for temporal networks: DynaWAVE. Here, we use graphlet-orbit transitions (GoTs), a different graphlet-based measure of temporal node similarity, as a new dynamic NC measure within DynaWAVE, resulting in GoT-WAVE.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>On synthetic networks, GoT-WAVE improves DynaWAVE\u2019s accuracy by 30% and speed by 64%. On real networks, when optimizing only dynamic NC, the methods are complementary. Furthermore, only GoT-WAVE supports directed edges. Hence, GoT-WAVE is a promising new temporal NA algorithm, which efficiently optimizes dynamic NC. We provide a user-friendly user interface and source code for GoT-WAVE.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>http:\/\/www.dcc.fc.up.pt\/got-wave\/<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btz119","type":"journal-article","created":{"date-parts":[[2019,2,13]],"date-time":"2019-02-13T21:19:21Z","timestamp":1550092761000},"page":"3527-3529","source":"Crossref","is-referenced-by-count":22,"title":["Temporal network alignment via GoT-WAVE"],"prefix":"10.1093","volume":"35","author":[{"given":"David","family":"Apar\u00edcio","sequence":"first","affiliation":[{"name":"CRACS & INESC-TEC, Departamento de Ci\u00eancia de Computadores, Faculdade de Ci\u00eancias, Universidade do Porto , Porto, Portugal"}]},{"given":"Pedro","family":"Ribeiro","sequence":"additional","affiliation":[{"name":"CRACS & INESC-TEC, Departamento de Ci\u00eancia de Computadores, Faculdade de Ci\u00eancias, Universidade do Porto , Porto, Portugal"}]},{"given":"Tijana","family":"Milenkovi\u0107","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Interdisciplinary Center for Network Science and Applications, and ECK Institute for Global Health, University of Notre Dame , Notre Dame, IN, USA"}]},{"given":"Fernando","family":"Silva","sequence":"additional","affiliation":[{"name":"CRACS & INESC-TEC, Departamento de Ci\u00eancia de Computadores, Faculdade de Ci\u00eancias, Universidade do Porto , Porto, Portugal"}]}],"member":"286","published-online":{"date-parts":[[2019,2,13]]},"reference":[{"key":"2023020108350658400_btz119-B1","doi-asserted-by":"crossref","first-page":"e0205497.","DOI":"10.1371\/journal.pone.0205497","article-title":"GoT: a fingerprint for temporal network comparison","volume":"13","author":"Apar\u00edcio","year":"2018","journal-title":"PLoS One"},{"key":"2023020108350658400_btz119-B2","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1186\/s13015-015-0050-8","article-title":"Fair evaluation of global network aligners","volume":"10","author":"Crawford","year":"2015","journal-title":"Algorithms Mol. Biol"},{"key":"2023020108350658400_btz119-B3","doi-asserted-by":"crossref","first-page":"i171","DOI":"10.1093\/bioinformatics\/btv227","article-title":"Exploring the structure and function of temporal networks with dynamic graphlets","volume":"31","author":"Hulovatyy","year":"2015","journal-title":"Bioinformatics"},{"key":"2023020108350658400_btz119-B4","first-page":"121.","article-title":"Optimal network alignment with graphlet degree vectors","volume":"9","author":"Milenkovi\u0107","year":"2010","journal-title":"Cancer Inf"},{"key":"2023020108350658400_btz119-B5","doi-asserted-by":"crossref","first-page":"e177","DOI":"10.1093\/bioinformatics\/btl301","article-title":"Biological network comparison using graphlet degree distribution","volume":"23","author":"Pr\u017eulj","year":"2007","journal-title":"Bioinformatics"},{"key":"2023020108350658400_btz119-B6","first-page":"16","volume-title":"Annual International Conference on Research in Computational Molecular Biology","author":"Singh","year":"2007"},{"key":"2023020108350658400_btz119-B7","first-page":"16","volume-title":"Wabi","author":"Sun","year":"2015"},{"key":"2023020108350658400_btz119-B8","doi-asserted-by":"crossref","first-page":"1795","DOI":"10.1093\/bioinformatics\/btx841","article-title":"Aligning dynamic networks with dynawave","volume":"34","author":"Vijayan","year":"2017","journal-title":"Bioinformatics"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/35\/18\/3527\/48975701\/bioinformatics_35_18_3527.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/35\/18\/3527\/48975701\/bioinformatics_35_18_3527.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T19:41:48Z","timestamp":1675280508000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/35\/18\/3527\/5317165"}},"subtitle":[],"editor":[{"given":"Bonnie","family":"Berger","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2019,2,13]]},"references-count":8,"journal-issue":{"issue":"18","published-print":{"date-parts":[[2019,9,15]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btz119","relation":{},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"type":"print","value":"1367-4803"},{"type":"electronic","value":"1367-4811"}],"subject":[],"published-other":{"date-parts":[[2019,9,15]]},"published":{"date-parts":[[2019,2,13]]}}}