{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T12:43:46Z","timestamp":1740141826940,"version":"3.37.3"},"reference-count":59,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"12","funder":[{"DOI":"10.13039\/501100001691","name":"JSPS KAKENHI","doi-asserted-by":"publisher","award":["JP23H03379"],"award-info":[{"award-number":["JP23H03379"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Commun."],"published-print":{"date-parts":[[2024,12]]},"DOI":"10.23919\/transcom.2024cei0008","type":"journal-article","created":{"date-parts":[[2024,8,22]],"date-time":"2024-08-22T18:12:15Z","timestamp":1724350335000},"page":"918-927","source":"Crossref","is-referenced-by-count":0,"title":["Random-Based and Deep Graph Generators: Evolution and Future Prospects"],"prefix":"10.23919","volume":"E107-B","author":[{"given":"Kohei","family":"Watabe","sequence":"first","affiliation":[{"name":"Graduate School of Science and Engineering, Saitama University,Saitama-shi,Japan,338-8570"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2012.12.008"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2015.01.083"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.physrep.2016.09.002"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599768"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-018-0287-6"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1039\/C9SC04503A"},{"article-title":"Generative code modeling with graphs","volume-title":"Proc. 7th International Conference on Learning Representations (ICLR 2019)","author":"Brockschmidt","key":"ref7"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.63.066117"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.5486\/PMD.1959.6.3-4.12"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1038\/30918"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1103\/RevModPhys.74.47"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3214832"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3098417"},{"key":"ref14","article-title":"Learning deep generative models of graphs","volume-title":"Proc. the 6th International Conference on Learning Representations (ICLR 2018) Workshop","author":"Li","year":"2018"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0277887"},{"journal-title":"GraphGUIDE: Interpretable and controllable conditional graph generation with discrete Bernoulli diffusion","year":"2023","author":"Tseng","key":"ref16"},{"key":"ref17","article-title":"Conditional structure generation through graph variational generative adversarial nets","volume-title":"Proc. 33rd Conference on Neural Information Processing Systems (NeurIPS 2019)","author":"Yang","year":"2019"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ICDCS51616.2021.00119"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TNSE.2023.3244590"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1080\/00018730601170527"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1145\/3379445"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.25080\/tcwv9851"},{"volume-title":"NetworkX - Network Analysis in Python","key":"ref23"},{"volume-title":"Igraph - The Network Analysis Package","key":"ref24"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1016\/0378-8733(83)90021-7"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1214\/009117904000000577"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611972740.43"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-04180-8_13"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1137\/130914218"},{"journal-title":"Darwini: Generating realistic large-scale social graphs","year":"2016","author":"Edunov","key":"ref30"},{"key":"ref31","article-title":"Auto-encoding variational bayes","volume-title":"Proc. 2nd International Conference on Learning Representations (ICLR 2014)","author":"Kingma","year":"2014"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.5555\/2969033.2969125"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP39728.2021.9413523"},{"key":"ref34","article-title":"Grammar variational autoencoder","volume-title":"Proc. 34th International Conference on Machine Learning (ICML 2017)","author":"Kusner","year":"2017"},{"key":"ref35","article-title":"Constrained graph variational autoencoders for molecule design","volume-title":"Proc. 32nd Conference on Neural Information Processing Systems (NeurIPS 2018)","author":"Liu","year":"2018"},{"key":"ref36","article-title":"Junction tree variational autoencoder for molecular graph generation","volume-title":"Proc. 35th International Conference on Machine Learning (ICML 2018)","author":"Jin","year":"2018"},{"key":"ref37","article-title":"MolGAN: An implicit generative model for small molecular graphs","volume-title":"Proc. 35th International Conference on Machine Learning (ICML 2018) Workshop","author":"Cao","year":"2018"},{"journal-title":"MolecularRNN: Generating realistic molecular graphs with optimized properties","year":"2019","author":"Popova","key":"ref38"},{"key":"ref39","article-title":"GraphAF: A flow-based autoregressive model for molecular graph generation","volume-title":"Proc. 8th International Conference on Learning Representations (ICLR 2020)","author":"Shi","year":"2020"},{"key":"ref40","article-title":"Hierarchical generation of molecular graphs using structural motifs","volume-title":"Proc. 37th International Conference on Machine Learning (ICML 2020)","author":"Jin","year":"2020"},{"key":"ref41","article-title":"Constrained generation of semantically valid graphs via regularizing variational autoencoders","volume-title":"Proc. 32nd Conference on Neural Information Processing Systems (NeurIPS 2018)","author":"Ma","year":"2018"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01418-6_41"},{"key":"ref43","article-title":"Conditional labeled graph generation with GANs","volume-title":"Proc. 7th International Conference on Learning Representations (ICLR 2019) Workshop","author":"Fan","year":"2019"},{"key":"ref44","article-title":"Variational graph auto-encoders","volume-title":"Proc. 30th Annual Conference on Neural Information Processing Systems (NIPS 2016) Workshop","author":"Kipf","year":"2016"},{"key":"ref45","article-title":"NetGAN: Generating graphs via random walks","volume-title":"Proc. 35th International Conference on Machine Learning (ICML 2018)","author":"Bojchevski","year":"2018"},{"key":"ref46","article-title":"Graphite: Iterative Generative Modeling of Graphs","volume-title":"Proc. 36th International Conference on Machine Learning (ICML 2019)","author":"Grover","year":"2019"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP40776.2020.9053451"},{"key":"ref48","article-title":"GraphRNN: Generating realistic graphs with deep auto-regressive models","volume-title":"Proc. 35th International Conference on Machine Learning (ICML 2018)","author":"You","year":"2018"},{"key":"ref49","article-title":"Graph generation with variational recurrent neural network","volume-title":"Proc. 33rd Conference on Neural Information Processing Systems (NeurIPS 2019) Workshop","author":"Su","year":"2019"},{"key":"ref50","article-title":"D-VAE: A variational autoencoder for directed acyclic graphs","volume-title":"Proc. 33rd Conference on Neural Information Processing Systems (NeurIPS 2019)","author":"Zhang","year":"2019"},{"key":"ref51","article-title":"Graph generation by sequential edge prediction","volume-title":"Proc. 27th European Symposium on Artificial Neural Networks (ESANN 2019)","author":"Bacciu","year":"2019"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.11.112"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380201"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2002.1184038"},{"journal-title":"Learn to generate time series conditioned graphs with generative adversarial nets","year":"2020","author":"Yang","key":"ref55"},{"key":"ref56","article-title":"Efficient graph generation with graph recurrent attention networks","volume-title":"Proc. 33rd Conference on Neural Information Processing Systems (NeurIPS 2019)","author":"Liao","year":"2019"},{"key":"ref57","article-title":"Disentangling interpretable generative parameters of random and real-world graphs","volume-title":"Proc. 33rd Conference on Neural Information Processing Systems (NeurIPS 2019) Workshop","author":"Stoehr","year":"2019"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/ICCCN58024.2023.10230174"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939754"}],"container-title":["IEICE Transactions on Communications"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/10400553\/10794625\/10643852.pdf?arnumber=10643852","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,12]],"date-time":"2024-12-12T07:39:53Z","timestamp":1733989193000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10643852\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12]]},"references-count":59,"journal-issue":{"issue":"12"},"URL":"https:\/\/doi.org\/10.23919\/transcom.2024cei0008","relation":{},"ISSN":["1745-1345","0916-8516"],"issn-type":[{"type":"electronic","value":"1745-1345"},{"type":"print","value":"0916-8516"}],"subject":[],"published":{"date-parts":[[2024,12]]}}}