{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T20:32:00Z","timestamp":1740169920319,"version":"3.37.3"},"reference-count":45,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"1","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1905211","61771140","62171132"],"award-info":[{"award-number":["U1905211","61771140","62171132"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Emerg. Topics Comput."],"published-print":{"date-parts":[[2024,1]]},"DOI":"10.1109\/tetc.2023.3268098","type":"journal-article","created":{"date-parts":[[2023,4,21]],"date-time":"2023-04-21T18:22:33Z","timestamp":1682101353000},"page":"139-149","source":"Crossref","is-referenced-by-count":0,"title":["Graph Reconfigurable Pooling for Graph Representation Learning"],"prefix":"10.1109","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-5205-9610","authenticated-orcid":false,"given":"Xiaolin","family":"Li","sequence":"first","affiliation":[{"name":"College of Computer and Cyber Security and Fujian Provincial Key Lab of Network Security and Cryptology, Fujian Normal University, Fuzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qikui","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Life Sciences, Fudan University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenyu","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Computer and Cyber Security and Fujian Provincial Key Lab of Network Security and Cryptology, Fujian Normal University, Fuzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongyan","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Computer and Cyber Security and Fujian Provincial Key Lab of Network Security and Cryptology, Fujian Normal University, Fuzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8972-3373","authenticated-orcid":false,"given":"Li","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Computer and Cyber Security and Fujian Provincial Key Lab of Network Security and Cryptology, Fujian Normal University, Fuzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2008.2005605"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.5555\/3327345.3327389"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330982"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TETC.2020.3027309"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TETC.2021.3132251"},{"key":"ref6","first-page":"5171","article-title":"Link prediction based on graph neural networks","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Zhang"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219890"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bti1007"},{"key":"ref9","first-page":"2224","article-title":"Convolutional networks on graphs for learning molecular fingerprints","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Duvenaud"},{"key":"ref10","first-page":"1106","article-title":"ImageNet classification with deep convolutional neural networks","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Krizhevsky"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM50108.2020.00039"},{"key":"ref12","first-page":"3734","article-title":"Self-attention graph pooling","volume-title":"Proc. 36th Int. Conf. Mach. Learn.","author":"Lee"},{"author":"Vinyals","key":"ref13","article-title":"Order matters: Sequence to sequence for sets"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11782"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00658"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-05933-9_15"},{"article-title":"The information bottleneck method","year":"2000","author":"Tishby","key":"ref17"},{"author":"Alemi","key":"ref18","article-title":"Deep variational information bottleneck"},{"key":"ref19","first-page":"20 437","article-title":"Graph information bottleneck","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Wu"},{"article-title":"Graph information bottleneck for subgraph recognition","year":"2020","author":"Yu","key":"ref20"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-00126-0_2"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00066"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.findings-acl.284"},{"key":"ref24","first-page":"15 524","article-title":"Interpretable and generalizable graph learning via stochastic attention mechanism","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Miao"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01879"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2021.3112205"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i4.20335"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1126\/science.286.5439.509"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1038\/30918"},{"article-title":"Variational graph auto-encoders","year":"2016","author":"Kipf","key":"ref30"},{"key":"ref31","first-page":"5694","article-title":"GraphRNN: Generating realistic graphs with deep auto-regressive models","volume-title":"Proc. 35th Int. Conf. Mach. Learn.","author":"You"},{"article-title":"MolGAN: An implicit generative model for small molecular graphs","year":"2018","author":"Cao","key":"ref32"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403104"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2022.11.083"},{"author":"Kingma","key":"ref35","article-title":"Auto-encoding variational bayes"},{"key":"ref36","first-page":"1278","article-title":"Stochastic backpropagation and approximate inference in deep generative models","volume-title":"Proc. 31th Int. Conf. Mach. Learn.","author":"Rezende"},{"key":"ref37","first-page":"1024","article-title":"Inductive representation learning on large graphs","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Hamilton"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE53745.2022.00125"},{"author":"Xu","key":"ref39","article-title":"How powerful are graph neural networks?"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1002\/cpa.3160360204"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1016\/S0022-2836(03)00628-4"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkh081"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-007-0103-5"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3450025"},{"author":"Kipf","key":"ref45","article-title":"Semi-supervised classification with graph convolutional networks"}],"container-title":["IEEE Transactions on Emerging Topics in Computing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6245516\/10474150\/10106645.pdf?arnumber=10106645","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,26]],"date-time":"2024-03-26T13:51:24Z","timestamp":1711461084000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10106645\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1]]},"references-count":45,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.1109\/tetc.2023.3268098","relation":{},"ISSN":["2168-6750","2376-4562"],"issn-type":[{"type":"electronic","value":"2168-6750"},{"type":"electronic","value":"2376-4562"}],"subject":[],"published":{"date-parts":[[2024,1]]}}}