{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T16:33:32Z","timestamp":1778258012264,"version":"3.51.4"},"reference-count":63,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"9","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":[{"DOI":"10.13039\/501100001381","name":"National Research Foundation Singapore","doi-asserted-by":"publisher","award":["MOE-T2EP20220-0006"],"award-info":[{"award-number":["MOE-T2EP20220-0006"]}],"id":[{"id":"10.13039\/501100001381","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Med. Imaging"],"published-print":{"date-parts":[[2024,9]]},"DOI":"10.1109\/tmi.2024.3392988","type":"journal-article","created":{"date-parts":[[2024,4,24]],"date-time":"2024-04-24T17:29:10Z","timestamp":1713979750000},"page":"3292-3305","source":"Crossref","is-referenced-by-count":37,"title":["Contrastive Graph Pooling for Explainable Classification of Brain Networks"],"prefix":"10.1109","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2498-5812","authenticated-orcid":false,"given":"Jiaxing","family":"Xu","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Nanyang Technological University, Jurong West, Singapore"}]},{"given":"Qingtian","family":"Bian","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanyang Technological University, Jurong West, Singapore"}]},{"given":"Xinhang","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Tsinghua University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5593-077X","authenticated-orcid":false,"given":"Aihu","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanyang Technological University, Jurong West, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9473-3202","authenticated-orcid":false,"given":"Yiping","family":"Ke","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanyang Technological University, Jurong West, Singapore"}]},{"given":"Miao","family":"Qiao","sequence":"additional","affiliation":[{"name":"School of Computer Science, The University of Auckland, Auckland, New Zealand"}]},{"given":"Wei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Cognitive Neuroimaging Centre and the Lee Kong Chian School of Medicine, Nanyang Technological University, Jurong West, Singapore"}]},{"given":"Wei","family":"Khang Jeremy Sim","sequence":"additional","affiliation":[{"name":"Cognitive Neuroimaging Centre and IGP-Neuroscience, Interdisciplinary Graduate Programme, Nanyang Technological University, Jurong West, Singapore"}]},{"given":"Bal\u00e1zs","family":"Guly\u00e1s","sequence":"additional","affiliation":[{"name":"Cognitive Neuroimaging Centre and the Lee Kong Chian School of Medicine, Nanyang Technological University, Jurong West, Singapore"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1006\/nimg.2001.0933"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9280.2009.02460.x"},{"key":"ref3","first-page":"1","article-title":"Data-driven network\n                        neuroscience: On data collection and benchmark","volume-title":"Proc. 37th Conf. Neural Inf. Process. Syst. Datasets Benchmarks\n                        Track","author":"Xu"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2016.09.046"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403383"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1609.02907"},{"key":"ref7","first-page":"1263","article-title":"Neural message passing for quantum\n                        chemistry","volume-title":"Proc. 34th ICML","volume":"70","author":"Gilmer"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i14.29551"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1145\/3535101"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1145\/3583780.3615512"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-66182-7_54"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-59728-3_61"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102233"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330921"},{"key":"ref15","first-page":"5812","article-title":"Graph contrastive learning with\n                        augmentations","volume-title":"Proc. Adv. Neural Inf.\n                        Process. Syst.","volume":"33","author":"You"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i8.20871"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i6.25858"},{"key":"ref18","article-title":"Graph attention networks","author":"Velickovic","year":"2017","journal-title":"arXiv:1710.10903"},{"key":"ref19","article-title":"How attentive are graph attention\n                        networks?","author":"Brody","year":"2021","journal-title":"arXiv:2105.14491"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098126"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098088"},{"key":"ref22","article-title":"Attention-based graph neural network for\n                        semi-supervised learning","author":"Thekumparampil","year":"2018","journal-title":"arXiv:1803.03735"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219980"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449822"},{"key":"ref25","first-page":"3734","article-title":"Self-attention graph pooling","volume-title":"Proc. ICML","author":"Lee"},{"key":"ref26","article-title":"Maximum entropy weighted independent set pooling for\n                        graph neural networks","author":"Nouranizadeh","year":"2021","journal-title":"arXiv:2107.01410"},{"key":"ref27","article-title":"Hierarchical graph pooling\n                        with structure learning","author":"Zhang","year":"2019","journal-title":"arXiv:1911.05954"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32254-0_54"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2021.3049199"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2022.3219260"},{"key":"ref31","article-title":"Parkinson\u2019s disease classification using\n                        contrastive graph cross-view learning with multimodal fusion of SPECT images\n                        and clinical features","author":"Ding","year":"2023","journal-title":"arXiv:2311.14902"},{"key":"ref32","first-page":"4314","article-title":"Learning dynamic graph representation of brain\n                        connectome with spatio-temporal attention","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Kim"},{"key":"ref33","article-title":"Learning task-aware effective\n                        brain connectivity for fMRI analysis with graph neural\n                        networks","author":"Yu","year":"2022","journal-title":"arXiv:2211.00261"},{"key":"ref34","article-title":"Brain network transformer","author":"Kan","year":"2022","journal-title":"arXiv:2210.06681"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0197121"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2019.02.062"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.3389\/conf.fninf.2013.09.00041"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-018-0235-4"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1093\/cercor\/bhx179"},{"key":"ref40","article-title":"How powerful are graph neural\n                        networks?","author":"Xu","year":"2018","journal-title":"arXiv:1810.00826"},{"key":"ref41","first-page":"1","article-title":"Attention is all you\n                        need","volume-title":"Proc. Adv. Neural Inf. Process.\n                        Syst.","volume":"30","author":"Vaswani"},{"key":"ref42","first-page":"1","article-title":"Hierarchical graph representation learning with\n                        differentiable pooling","volume-title":"Proc. Adv. Neural\n                        Inf. Process. Syst.","volume":"31","author":"Ying"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1958.tb00292.x"},{"key":"ref44","first-page":"2825","article-title":"Scikit-learn: Machine\n                        learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn.\n                        Res."},{"key":"ref45","first-page":"1","article-title":"Inductive representation learning on large\n                        graphs","volume-title":"Proc. Adv. Neural Inf. Process.\n                        Syst.","volume":"30","author":"Hamilton"},{"key":"ref46","article-title":"Residual gated graph\n                    ConvNets","author":"Bresson","year":"2017","journal-title":"arXiv:1711.07553"},{"key":"ref47","article-title":"Benchmarking graph neural\n                    networks","author":"Dwivedi","year":"2020","journal-title":"arXiv:2003.00982"},{"key":"ref48","article-title":"Adam: A method for stochastic\n                        optimization","author":"Kingma","year":"2014","journal-title":"arXiv:1412.6980"},{"key":"ref49","first-page":"1","article-title":"Automatic differentiation in\n                        PyTorch","volume-title":"Proc. NIPS","author":"Paszke"},{"key":"ref50","first-page":"1","article-title":"Deep graph library: Towards\n                        efficient and scalable deep learning on graphs","volume-title":"Proc. ICLR","author":"Wang"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1016\/j.brainres.2009.11.057"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2010.05.067"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuropsychologia.2017.06.008"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2005.12.045"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1523\/JNEUROSCI.5698-11.2012"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1007\/s00406-011-0226-2"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1016\/j.cortex.2015.03.016"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1523\/JNEUROSCI.4860-03.2004"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuropsychologia.2014.12.022"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1002\/hbm.22747"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.0604187103"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1159\/000058331"},{"issue":"86","key":"ref63","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Van der Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."}],"container-title":["IEEE Transactions on Medical Imaging"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/42\/10663877\/10508252.pdf?arnumber=10508252","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T00:08:03Z","timestamp":1732666083000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10508252\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9]]},"references-count":63,"journal-issue":{"issue":"9"},"URL":"https:\/\/doi.org\/10.1109\/tmi.2024.3392988","relation":{},"ISSN":["0278-0062","1558-254X"],"issn-type":[{"value":"0278-0062","type":"print"},{"value":"1558-254X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9]]}}}