{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T13:21:22Z","timestamp":1771680082322,"version":"3.50.1"},"reference-count":20,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"5","license":[{"start":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T00:00:00Z","timestamp":1725148800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T00:00:00Z","timestamp":1725148800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T00:00:00Z","timestamp":1725148800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Ministry of Science and Technology of China","award":["2020AAA0108405"],"award-info":[{"award-number":["2020AAA0108405"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["72293575"],"award-info":[{"award-number":["72293575"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Intell. Syst."],"published-print":{"date-parts":[[2024,9]]},"DOI":"10.1109\/mis.2024.3391937","type":"journal-article","created":{"date-parts":[[2024,4,24]],"date-time":"2024-04-24T17:36:54Z","timestamp":1713980214000},"page":"37-46","source":"Crossref","is-referenced-by-count":3,"title":["Efficient Spiking Variational Graph Autoencoders for Unsupervised Graph Representation Learning Tasks"],"prefix":"10.1109","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4473-2356","authenticated-orcid":false,"given":"Hanxuan","family":"Yang","sequence":"first","affiliation":[{"name":"University of Chinese Academy of Sciences, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1929-8404","authenticated-orcid":false,"given":"Qingchao","family":"Kong","sequence":"additional","affiliation":[{"name":"University of Chinese Academy of Sciences, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9391-5060","authenticated-orcid":false,"given":"Ruike","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of Chinese Academy of Sciences, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2323-5091","authenticated-orcid":false,"given":"Wenji","family":"Mao","sequence":"additional","affiliation":[{"name":"University of Chinese Academy of Sciences, Beijing, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.166"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1145\/3394171.3413661"},{"key":"ref3","article-title":"Semi-supervised classification with graph convolutional networks","author":"Kipf","year":"2017"},{"key":"ref4","article-title":"Variational graph auto-encoders","author":"Kipf","year":"2016"},{"key":"ref5","first-page":"5274","article-title":"Dirichlet graph variational autoencoder","volume-title":"Proc. 33rd Adv. Neural Inf. Process. Syst. (NeurIPS)","author":"Li","year":"2020"},{"key":"ref6","article-title":"A graph is worth 1-bit spikes: When graph contrastive learning meets spiking neural networks","author":"Li","year":"2024"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-021-00311-4"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i6.20665"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/jsen.2023.3329559"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/tcds.2023.3338614"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/441"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/338"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2022.3183143"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2024.3370918"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599546"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2021.04.015"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i7.26034"},{"key":"ref18","first-page":"22118","article-title":"Open graph benchmark: Datasets for machine learning on graphs","volume-title":"Proc. 33rd Adv. Neural Inf. Proc. Syst. (NeurIPS)","author":"Hu","year":"2020"},{"key":"ref19","first-page":"21019","article-title":"Deconvolutional networks on graph data","volume-title":"Proc. 34th Adv. Neural Inf. Proc. Syst. (NeurIPS)","author":"Li","year":"2021"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/isscc.2014.6757323"}],"container-title":["IEEE Intelligent Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9670\/10701577\/10507834.pdf?arnumber=10507834","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,3]],"date-time":"2024-10-03T17:30:39Z","timestamp":1727976639000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10507834\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9]]},"references-count":20,"journal-issue":{"issue":"5"},"URL":"https:\/\/doi.org\/10.1109\/mis.2024.3391937","relation":{},"ISSN":["1541-1672","1941-1294"],"issn-type":[{"value":"1541-1672","type":"print"},{"value":"1941-1294","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9]]}}}