{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T21:15:43Z","timestamp":1768338943903,"version":"3.49.0"},"reference-count":49,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. on Signal and Inf. Process. over Networks"],"published-print":{"date-parts":[[2021]]},"DOI":"10.1109\/tsipn.2020.3041940","type":"journal-article","created":{"date-parts":[[2020,12,2]],"date-time":"2020-12-02T22:56:28Z","timestamp":1606949788000},"page":"17-29","source":"Crossref","is-referenced-by-count":6,"title":["node2coords: Graph Representation Learning with Wasserstein Barycenters"],"prefix":"10.1109","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1180-1722","authenticated-orcid":false,"given":"Effrosyni","family":"Simou","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2319-4832","authenticated-orcid":false,"given":"Dorina","family":"Thanou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4010-714X","authenticated-orcid":false,"given":"Pascal","family":"Frossard","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","first-page":"3844","article-title":"Convolutional neural networks on graphs with fast localized spectral filtering","author":"defferrard","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1137\/141000439"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1137\/060659624"},{"key":"ref32","first-page":"2292","article-title":"Sinkhorn distances: Lightspeed computation of optimal transport","author":"cuturi","year":"0","journal-title":"Proc Adv Neural Inf Process Systems"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1007\/BF01593804"},{"key":"ref30","first-page":"227","article-title":"On translation of mass","volume":"37","author":"kantorovich","year":"1942","journal-title":"Doklady Akademii Nauk SSSR"},{"key":"ref37","first-page":"1407","article-title":"Wasserstein regularization for sparse multi-task regression","author":"janati","year":"0","journal-title":"Proc 22nd Int Conf Artif Intell Statist"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1137\/100805741"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177729694"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1090\/mcom\/3303"},{"key":"ref28","volume":"338","author":"villani","year":"0","journal-title":"Optimal Transport Old and New"},{"key":"ref27","first-page":"1542","article-title":"Graph optimal transport for cross-domain alignment","author":"chen","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref29","article-title":"M&#x00E9;moire sur la th&#x00E9;orie des d&#x00E9;blais et des remblais","author":"monge","year":"0","journal-title":"Histoire de l&#x2019;Acad&#x00E9;mie Royale des Sciences de Paris"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511790423"},{"key":"ref1","author":"chung","year":"1997","journal-title":"Spectral Graph Theory"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939753"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1007\/s13278-020-00649-4"},{"key":"ref21","first-page":"1145","article-title":"Deep neural networks for learning graph representations","author":"cao","year":"0","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1137\/17M1140431"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220052"},{"key":"ref26","first-page":"13 876","article-title":"GOT: An optimal transport framework for graph comparison","author":"maretic","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref25","first-page":"1601","article-title":"Hierarchical optimal transport for document representation","author":"yurochkin","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref10","doi-asserted-by":"crossref","first-page":"2319","DOI":"10.1126\/science.290.5500.2319","article-title":"A global geometric framework for nonlinear dimensionality reduction","volume":"290","author":"tenenbaum","year":"2000","journal-title":"Science"},{"key":"ref11","doi-asserted-by":"crossref","first-page":"2323","DOI":"10.1126\/science.290.5500.2323","article-title":"Nonlinear dimensionality reduction by locally linear embedding","volume":"290","author":"roweis","year":"2000","journal-title":"Science"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1016\/j.acha.2006.04.006"},{"key":"ref12","first-page":"585","article-title":"Laplacian eigenmaps and spectral techniques for embedding and clustering","author":"belkin","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1145\/2488388.2488393"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939751"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/2806416.2806512"},{"key":"ref16","first-page":"1222","author":"guthrie","year":"0","journal-title":"Proc Int Conf Lang Resour Eval"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623732"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939754"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1145\/2736277.2741093"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/3184558.3191523"},{"key":"ref3","author":"manning","year":"1999","journal-title":"Foundations of Statistical Natural Language Processing"},{"key":"ref6","first-page":"8317","article-title":"Certifiable robustness to graph perturbations","author":"bojchevski","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220001"},{"key":"ref8","article-title":"Machine learning on graphs: A model and comprehensive taxonomy","author":"chami","year":"2020","journal-title":"arXiv 2005 03675"},{"key":"ref7","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1145\/2897824.2925918","volume":"35","author":"bonneel","year":"2016","journal-title":"ACM Trans Graph"},{"key":"ref49","first-page":"2837","article-title":"Information theoretic measures for clusterings comparison: Variants, properties, normalization and correction for chance","volume":"11","author":"vinh","year":"2010","journal-title":"J Mach Learn Res"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2018.2807452"},{"key":"ref46","year":"2004","journal-title":"Orgnet"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1086\/jar.33.4.3629752"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1016\/0378-8733(83)90021-7"},{"key":"ref47","article-title":"The network data repository with interactive graph analytics and visualization","author":"rossi","year":"2015","journal-title":"AAAI Conf Artif Intell"},{"key":"ref42","first-page":"3530","article-title":"On the complexity of approximating wasserstein barycenters","author":"kroshnin","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref41","article-title":"Automatic differentiation in pytorch","author":"paszke","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref44","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1007\/978-3-642-35289-8_3","article-title":"Efficient backprop","author":"lecun","year":"2012","journal-title":"Neural Networks Tricks of the Trade"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2019.8683335"}],"container-title":["IEEE Transactions on Signal and Information Processing over Networks"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6884276\/9311902\/09277926.pdf?arnumber=9277926","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T14:54:15Z","timestamp":1652194455000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9277926\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"references-count":49,"URL":"https:\/\/doi.org\/10.1109\/tsipn.2020.3041940","relation":{},"ISSN":["2373-776X","2373-7778"],"issn-type":[{"value":"2373-776X","type":"electronic"},{"value":"2373-7778","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]}}}