{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T21:21:17Z","timestamp":1772918477702,"version":"3.50.1"},"reference-count":90,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"4","license":[{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Independent Research Fund Denmark","award":["0136-00315B"],"award-info":[{"award-number":["0136-00315B"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Knowl. Data Eng."],"published-print":{"date-parts":[[2024,4]]},"DOI":"10.1109\/tkde.2023.3304344","type":"journal-article","created":{"date-parts":[[2023,8,28]],"date-time":"2023-08-28T18:05:31Z","timestamp":1693245931000},"page":"1399-1412","source":"Crossref","is-referenced-by-count":3,"title":["A Hierarchical Block Distance Model for Ultra Low-Dimensional Graph Representations"],"prefix":"10.1109","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9311-3458","authenticated-orcid":false,"given":"Nikolaos","family":"Nakis","sequence":"first","affiliation":[{"name":"Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8912-711X","authenticated-orcid":false,"given":"Abdulkadir","family":"\u00c7elikkanat","sequence":"additional","affiliation":[{"name":"Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6099-2345","authenticated-orcid":false,"given":"Sune","family":"Lehmann","sequence":"additional","affiliation":[{"name":"Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4985-4368","authenticated-orcid":false,"given":"Morten","family":"M\u00f8rup","sequence":"additional","affiliation":[{"name":"Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1137\/S003614450342480"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1145\/956863.956972"},{"key":"ref3","doi-asserted-by":"crossref","DOI":"10.7551\/mitpress\/7432.001.0001","volume-title":"Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)","author":"Getoor","year":"2007"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939754"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.physrep.2009.11.002"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2018.2850013"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623732"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1145\/2736277.2741093"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5737"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/BigData.2018.8621872"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1310.4546"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1145\/2806416.2806512"},{"key":"ref13","first-page":"1025","article-title":"Inductive representation learning on large graphs","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Hamilton"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/594"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313446"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/3336191.3371800"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11849"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2018.00094"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1051\/proc\/201447004"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2014.988214"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1214\/16-aoas955"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2016.2591009"},{"key":"ref23","article-title":"Piecewise-velocity model for learning continuous-time dynamic node representations","author":"\u00c7elikkanat","year":"2022"},{"key":"ref24","first-page":"1145","article-title":"Dynamic social network analysis using latent space models","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Sarkar"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1214\/19-STS702"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.15446\/rce.v44n1.89369"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1214\/18-ss121"},{"key":"ref28","article-title":"HM-LDM: A hybrid-membership latent distance model","author":"Nakis","year":"2022"},{"key":"ref29","article-title":"Characterizing polarization in social networks using the signed relational latent distance model","author":"Nakis","year":"2023"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1198\/016214502388618906"},{"issue":"1","key":"ref31","first-page":"45","article-title":"Personal network integration: Transitivity and homophily in strong-tie relations","volume-title":"Social Netw.","volume":"22","author":"Louch","year":"2000"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1002\/asi.23916"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1017\/nws.2014.17"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1017\/cbo9780511894701"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1016\/j.socnet.2009.04.001"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1037\/h0046049"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1606295113"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-985X.2007.00471.x"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1016\/0378-8733(83)90021-7"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1198\/016214501753208735"},{"key":"ref41","first-page":"657","article-title":"Modeling homophily and stochastic equivalence in symmetric relational data","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Hoff"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1080\/10618600.2012.679240"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.67.026112"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1038\/nature06830"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/7503.003.0153"},{"key":"ref46","first-page":"1377","article-title":"The Mondrian process","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Roy"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/CIP.2012.6232913"},{"key":"ref48","first-page":"960","article-title":"Modeling temporal evolution and multiscale structure in networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Herlau"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevX.4.011047"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1088\/1742-5468\/2008\/10\/P10008"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1038\/nature09182"},{"key":"ref52","first-page":"1601","article-title":"Bayesian hierarchical community discovery","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Blundell"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2020.1833888"},{"key":"ref54","first-page":"33","article-title":"Hierarchical spectral partitioning of bipartite graphs to cluster dialects and identify distinguishing features","volume-title":"Proc. Workshop Graph-Based Methods Natural Lang. Process.","author":"Wieling"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1145\/502512.502550"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/TNSE.2018.2830822"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.0611034104"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1162\/NECO_a_00314"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1198\/016214504000001015"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/MLSP.2012.6349745"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.83.016107"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.90.032819"},{"key":"ref63","volume-title":"Network science","author":"Barab\u00e1si","year":"2016"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1086\/226141"},{"key":"ref65","volume-title":"Discrete Mathematics With Applications","author":"Epp","year":"2010"},{"key":"ref66","article-title":"An LP norm minimization using auxiliary function for compressed sensing","volume-title":"Proc. Int. Multiconf. Comp. Sci. Inf. Technol.","author":"Tsutsu"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.2307\/2346830"},{"key":"ref68","doi-asserted-by":"crossref","DOI":"10.1145\/3178876.3186120","article-title":"VERSE","volume-title":"Proc. World Wide Web Conf.","author":"Tsitsulin"},{"key":"ref69","article-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2017"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1609\/aimag.v29i3.2157"},{"key":"ref71","first-page":"539","article-title":"Learning to discover social circles in ego networks","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Leskovec"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-013-0693-z"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1145\/1298306.1298311"},{"key":"ref74","article-title":"Social computing data repository at ASU","author":"Zafarani","year":"2009"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1145\/3110025.3110086"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1145\/1217299.1217301"},{"key":"ref77","article-title":"SNAP Datasets: Stanford large network dataset collection","author":"Leskovec","year":"2014"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1145\/3159652.3159706"},{"key":"ref79","article-title":"The hardness of k-means clustering","author":"Dasgupta","year":"2008"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.1016\/j.tcs.2010.05.034"},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkx1037"},{"key":"ref82","article-title":"2009 Github challenge","author":"Chacon","year":"2009"},{"key":"ref83","first-page":"361","article-title":"Rcv1: A new benchmark collection for text categorization research","volume":"5","author":"Lewis","year":"2004","journal-title":"J. Mach. Learn. Res."},{"key":"ref84","first-page":"147","article-title":"Using the triangle inequality to accelerate k-means","volume-title":"Proc. 20th Int. Conf. Mach. Learn.","author":"Elkan"},{"key":"ref85","doi-asserted-by":"publisher","DOI":"10.1109\/ICMLA.2010.46"},{"issue":"86","key":"ref86","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"van der Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref87","article-title":"Node embeddings and exact low-rank representations of complex networks","author":"Chanpuriya","year":"2020"},{"key":"ref88","article-title":"Neural embeddings of graphs in hyperbolic space","volume-title":"Proc. 13th Int. Workshop Mining Learn. Graphs","author":"Ben Chamberlain"},{"key":"ref89","first-page":"6341","article-title":"Poincar\u00e9 embeddings for learning hierarchical representations","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Nickel"},{"key":"ref90","first-page":"1025","article-title":"Inductive representation learning on large graphs","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Hamilton"}],"container-title":["IEEE Transactions on Knowledge and Data Engineering"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/69\/10462568\/10233071.pdf?arnumber=10233071","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,8]],"date-time":"2024-03-08T02:34:40Z","timestamp":1709865280000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10233071\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4]]},"references-count":90,"journal-issue":{"issue":"4"},"URL":"https:\/\/doi.org\/10.1109\/tkde.2023.3304344","relation":{},"ISSN":["1041-4347","1558-2191","2326-3865"],"issn-type":[{"value":"1041-4347","type":"print"},{"value":"1558-2191","type":"electronic"},{"value":"2326-3865","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4]]}}}