{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T20:00:27Z","timestamp":1785355227090,"version":"3.55.0"},"reference-count":34,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"NVIDIA Academic Grant Program"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2026]]},"DOI":"10.1109\/access.2026.3712766","type":"journal-article","created":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T20:05:44Z","timestamp":1783973144000},"page":"111227-111239","source":"Crossref","is-referenced-by-count":0,"title":["Single-Stage Graph-Based Floorplan Generation"],"prefix":"10.1109","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9013-5002","authenticated-orcid":false,"given":"Yusuke","family":"Takeuchi","sequence":"first","affiliation":[{"name":"1Tetraz Inc.","place":["Tokyo, Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7641-2632","authenticated-orcid":false,"given":"Qi","family":"An","sequence":"additional","affiliation":[{"name":"The University of Tokyo","place":["Chiba, Japan"],"department":["3Department of Human and Engineered Environmental Studies"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1280-069X","authenticated-orcid":false,"given":"Atsushi","family":"Yamashita","sequence":"additional","affiliation":[{"name":"The University of Tokyo","place":["Chiba, Japan"],"department":["3Department of Human and Engineered Environmental Studies"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00529"},{"key":"ref2","first-page":"6840","article-title":"Denoising diffusion probabilistic models","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Ho"},{"key":"ref3","first-page":"8780","article-title":"Diffusion models beat GANs on image synthesis","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Dhariwal"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v39i16.33904"},{"key":"ref5","article-title":"Advancing graph generation through beta diffusion","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Liu"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.52202\/075280-1309"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1145\/3757374.3771518"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1145\/3386569.3392391"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/3355089.3356556"},{"key":"ref12","first-page":"2672","article-title":"Generative adversarial networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"27","author":"Goodfellow"},{"key":"ref13","article-title":"LayoutGAN: Generating graphic layouts with wireframe discriminators","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Li"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58452-8_10"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01342"},{"key":"ref16","first-page":"2256","article-title":"Deep unsupervised learning using nonequilibrium thermodynamics","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Sohl-Dickstein"},{"key":"ref17","article-title":"Denoising diffusion implicit models","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Song"},{"key":"ref18","first-page":"8162","article-title":"Improved denoising diffusion probabilistic models","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Nichol"},{"key":"ref19","article-title":"Score-based generative modeling through stochastic differential equations","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Song"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1145\/3664647.3680681"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1145\/3681756.3697884"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1145\/3528223.3530135"},{"key":"ref23","first-page":"5708","article-title":"GraphRNN: Generating realistic graphs with deep auto-regressive models","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"You"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01418-6_41"},{"key":"ref25","article-title":"DiGress: Discrete denoising diffusion for graph generation","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Vignac"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2025.3546874"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2025.118794"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"key":"ref29","article-title":"Semi-supervised classification with graph convolutional networks","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Kipf"},{"key":"ref30","article-title":"Layer normalization","author":"Lei Ba","year":"2016","journal-title":"arXiv:1607.06450"},{"key":"ref31","article-title":"A generalization of transformer networks to graphs","volume-title":"Proc. AAAI Workshop Deep Learn. Graphs: Methods Appl.","author":"Dwivedi"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11671"},{"key":"ref33","first-page":"1","article-title":"Adam: A method for stochastic optimization","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Kingma"},{"key":"ref34","first-page":"6626","article-title":"GANs trained by a two time-scale update rule converge to a local Nash equilibrium","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Heusel"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/11323511\/11606379.pdf?arnumber=11606379","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T19:09:44Z","timestamp":1785352184000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11606379\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":34,"URL":"https:\/\/doi.org\/10.1109\/access.2026.3712766","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]}}}