{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T12:48:35Z","timestamp":1783687715714,"version":"3.55.0"},"reference-count":40,"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":[{"name":"STI 2030-Major Projects","award":["2021ZD0200403"],"award-info":[{"award-number":["2021ZD0200403"]}]},{"name":"National Key R&D Program of China","award":["2018AAA0100100"],"award-info":[{"award-number":["2018AAA0100100"]}]},{"name":"National Key R&D Program of China","award":["2018YFA0902600"],"award-info":[{"award-number":["2018YFA0902600"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61932008"],"award-info":[{"award-number":["61932008"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U22A2039"],"award-info":[{"award-number":["U22A2039"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62002266"],"award-info":[{"award-number":["62002266"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62073231"],"award-info":[{"award-number":["62073231"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Project of Science and Technology of Guangxi","award":["2021AB20147"],"award-info":[{"award-number":["2021AB20147"]}]},{"name":"Guangxi Natural Science Foundation","award":["2022JJD170019"],"award-info":[{"award-number":["2022JJD170019"]}]},{"name":"Guangxi Natural Science Foundation","award":["2021JJA170204"],"award-info":[{"award-number":["2021JJA170204"]}]},{"name":"Guangxi Natural Science Foundation","award":["2021JJA170199"],"award-info":[{"award-number":["2021JJA170199"]}]},{"name":"Guangxi Science and Technology Base and Talents Special Project","award":["2021AC19354"],"award-info":[{"award-number":["2021AC19354"]}]},{"name":"Guangxi Science and Technology Base and Talents Special Project","award":["2021AC19394"],"award-info":[{"award-number":["2021AC19394"]}]},{"name":"Guangxi Key Lab of Human-machine Interaction and Intelligent Decision, Guangxi Academy Sciences"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Netw. Sci. Eng."],"published-print":{"date-parts":[[2024,1]]},"DOI":"10.1109\/tnse.2023.3300878","type":"journal-article","created":{"date-parts":[[2023,8,3]],"date-time":"2023-08-03T17:40:23Z","timestamp":1691084423000},"page":"463-470","source":"Crossref","is-referenced-by-count":24,"title":["SSPool: A Simple Siamese Framework for Graph Infomax Pooling"],"prefix":"10.1109","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4135-0838","authenticated-orcid":false,"given":"Zhi-Peng","family":"Li","sequence":"first","affiliation":[{"name":"Institute of Machine Learning and Systems Biology, School of Electronics and Information Engineering, Tongji University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4759-9105","authenticated-orcid":false,"given":"Hai-Long","family":"Su","sequence":"additional","affiliation":[{"name":"Institute of Machine Learning and Systems Biology, School of Electronics and Information Engineering, Tongji University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7812-9408","authenticated-orcid":false,"given":"Xiao-Bo","family":"Zhu","sequence":"additional","affiliation":[{"name":"Institute of Machine Learning and Systems Biology, School of Electronics and Information Engineering, Tongji University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9393-351X","authenticated-orcid":false,"given":"Valeriya","family":"Gribova","sequence":"additional","affiliation":[{"name":"Institute of Automation and Control Processes, Far Eastern Branch of Russian Academy of Sciences, Vladivostok, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8900-8081","authenticated-orcid":false,"given":"Vladimir Fedorovich","family":"Filaretov","sequence":"additional","affiliation":[{"name":"Institute of Automation and Control Processes, Far Eastern Branch of Russian Academy of Sciences, Vladivostok, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6759-2691","authenticated-orcid":false,"given":"De-Shuang","family":"Huang","sequence":"additional","affiliation":[{"name":"Eastern Institute of Technology, Ningbo, Zhejiang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219890"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449813"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-022-00447-x"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2020.01.021"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00532"},{"key":"ref6","first-page":"4800","article-title":"Hierarchical graph representation learning with differentiable pooling","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Ying"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11849"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11782"},{"key":"ref9","first-page":"1","article-title":"How powerful are graph neural networks","volume-title":"Proc. 7th Int. Conf. Learn. Representations","author":"Xu","year":"2019"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5997"},{"key":"ref11","first-page":"1","article-title":"StructPool: Structured graph pooling via conditional random fields","volume-title":"Proc. 8th Int. Conf. Learn. Representations","author":"Yuan"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2021.3081010"},{"key":"ref13","first-page":"3734","article-title":"Self-attention graph pooling","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Lee"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2021.3062794"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3463074"},{"key":"ref16","first-page":"1","article-title":"Spectral networks and locally connected networks on graphs","volume-title":"Proc. 2nd Int. Conf. Learn. Representations","author":"Bruna","year":"2014"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1606.09375"},{"key":"ref18","first-page":"1","article-title":"Semi-supervised classification with graph convolutional networks","volume-title":"Proc. 5th Int. Conf. Learn. Representations","author":"Kipf","year":"2017"},{"key":"ref19","first-page":"1025","article-title":"Inductive representation learning on large graphs","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Hamilton"},{"key":"ref20","first-page":"1","article-title":"Graph attention networks","volume-title":"Proc. 5th Int. Conf. Learn. Representations","author":"Velikovi","year":"2018"},{"key":"ref21","first-page":"1","article-title":"Order matters: Sequence to sequence for sets","volume-title":"Proc. 4th Int. Conf. Learn. Representations","author":"Vinyals","year":"2016"},{"key":"ref22","first-page":"14093","article-title":"Graph cross networks with vertex infomax pooling","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Li"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01549"},{"key":"ref24","first-page":"531","article-title":"Mutual information neural estimation","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Belghazi"},{"key":"ref25","first-page":"1","article-title":"Learning deep representations by mutual information estimation and maximization","volume-title":"Proc. 7th Int. Conf. Learn. Representations","author":"Hjelm","year":"2019"},{"key":"ref26","first-page":"1","article-title":"Deep graph infomax","volume-title":"Proc. 7th Int. Conf. Learn. Representations","author":"Velikovi","year":"2019"},{"key":"ref27","first-page":"5812","article-title":"Graph contrastive learning with augmentations","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"You"},{"key":"ref28","article-title":"Deep graph contrastive representation learning","author":"Zhu","year":"2020"},{"key":"ref29","first-page":"1","article-title":"Bootstrapped representation learning on graphs","volume-title":"Proc. Int. Conf. Learn. Representations Workshop Geometrical Topological Representation Learn.","author":"Thakoor","year":"2021"},{"key":"ref30","first-page":"76","article-title":"From canonical correlation analysis to self-supervised graph neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Zhang"},{"key":"ref31","article-title":"Representation learning with contrastive predictive coding","author":"Oord","year":"2018"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.5555\/3495724.3497510"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1016\/S0022-2836(03)00628-4"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bti1007"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-007-0103-5"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1021\/jm040835a"},{"key":"ref37","first-page":"2134","article-title":"A structural smoothing framework for robust graph comparison","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Yanardag"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.2307\/j.ctvcm4g18.8"},{"key":"ref39","first-page":"1","article-title":"Fast graph representation learning with PyTorch Geometric","volume-title":"Proc. Int. Conf. Learn. Representations Workshop","author":"Fey","year":"2021"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33014602"}],"container-title":["IEEE Transactions on Network Science and Engineering"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6488902\/10382160\/10207695.pdf?arnumber=10207695","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,12]],"date-time":"2024-12-12T19:22:17Z","timestamp":1734031337000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10207695\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1]]},"references-count":40,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.1109\/tnse.2023.3300878","relation":{},"ISSN":["2327-4697","2334-329X"],"issn-type":[{"value":"2327-4697","type":"electronic"},{"value":"2334-329X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1]]}}}