{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T10:04:15Z","timestamp":1775815455125,"version":"3.50.1"},"reference-count":83,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"11","license":[{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"National Key R&D Program of China","award":["2022YFB4501704"],"award-info":[{"award-number":["2022YFB4501704"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62102287"],"award-info":[{"award-number":["62102287"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shanghai Science and Technology Innovation Action Plan","award":["22YS1400600"],"award-info":[{"award-number":["22YS1400600"]}]},{"name":"Shanghai Science and Technology Innovation Action Plan","award":["22511100700"],"award-info":[{"award-number":["22511100700"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Knowl. Data Eng."],"published-print":{"date-parts":[[2024,11]]},"DOI":"10.1109\/tkde.2024.3409869","type":"journal-article","created":{"date-parts":[[2024,6,5]],"date-time":"2024-06-05T18:01:32Z","timestamp":1717610492000},"page":"5576-5590","source":"Crossref","is-referenced-by-count":17,"title":["AdaRisk: Risk-Adaptive Deep Reinforcement Learning for Vulnerable Nodes Detection"],"prefix":"10.1109","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-2951-2226","authenticated-orcid":false,"given":"Fan","family":"Li","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-1701-0240","authenticated-orcid":false,"given":"Zhiyu","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Tongji University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5877-7387","authenticated-orcid":false,"given":"Dawei","family":"Cheng","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Tongji University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3554-3219","authenticated-orcid":false,"given":"Xiaoyang","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9780511815478","volume-title":"Social Network Analysis: Methods and Applications","author":"Wasserman","year":"1994"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2022.3162339"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2006.874531"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2010.80"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1101\/gr.2203804"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/INFCOM.2007.201"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3094549"},{"key":"ref8","first-page":"7565","article-title":"Controlling graph dynamics with reinforcement learning and graph neural networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Meirom"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1038\/scientificamerican0507-60"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1145\/956750.956769"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/1557019.1557047"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2015.2419659"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380028"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1145\/2723372.2723734"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2015.64"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ASONAM49781.2020.9381460"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1145\/3437963.3441829"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403076"},{"key":"ref19","first-page":"22092","article-title":"Graph random neural networks for semi-supervised learning on graphs","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Feng"},{"key":"ref20","article-title":"Improving graph attention networks with large margin-based constraints","author":"Wang","year":"2019"},{"key":"ref21","first-page":"5453","article-title":"Representation learning on graphs with jumping knowledge networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Xu"},{"key":"ref22","first-page":"11592","article-title":"Optimization of graph neural networks: Implicit acceleration by skip connections and more depth","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Xu"},{"key":"ref23","first-page":"1039","article-title":"GEP-PG: Decoupling exploration and exploitation in deep reinforcement learning algorithms","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Colas"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1145\/1102351.1102377"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.32657\/10356\/90191"},{"key":"ref26","first-page":"1146","article-title":"Stabilising experience replay for deep multi-agent reinforcement learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Foerster"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/MLSP.2016.7738878"},{"key":"ref28","first-page":"1352","article-title":"Reinforcement learning with deep energy-based policies","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Haarnoja"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1145\/3341302.3342080"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1145\/1217299.1217301"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2016.0033"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1145\/1772690.1772756"},{"key":"ref33","article-title":"Query-driven active surveying for collective classification","volume-title":"Proc. 10th Int. Workshop Mining Learn. Graphs","author":"Namata"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2020\/618"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2023\/681"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/807"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i12.26702"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.58886\/jfi.v13i2.2499"},{"key":"ref39","first-page":"71","article-title":"Researches on risks and precautions of Chinese P2P lending","volume":"28","author":"Liu","year":"2017","journal-title":"Manage. Eng."},{"key":"ref40","first-page":"6861","article-title":"Simplifying graph convolutional networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Wu"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1007\/s11704-022-1531-9"},{"key":"ref42","article-title":"How powerful are graph neural networks?","author":"Xu","year":"2018"},{"key":"ref43","article-title":"How attentive are graph attention networks?","author":"Brody","year":"2021"},{"key":"ref44","first-page":"14501","article-title":"Recipe for a general, powerful, scalable graph transformer","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Ramp\u00e1\u0161ek"},{"key":"ref45","first-page":"1","article-title":"Anti-symmetric DGN: A stable architecture for deep graph networks","volume-title":"Proc. 11th Int. Conf. Learn. Representations","author":"Gravina"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583408"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1145\/3581783.3612566"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1145\/3183713.3183749"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.14778\/3324301.3324304"},{"key":"ref50","first-page":"1","article-title":"Adam: A method for stochastic optimization","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Kingma"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.3115\/1626394.1626406"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2010.118"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1145\/2588555.2593670"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623746"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1145\/3132847.3132973"},{"key":"ref56","first-page":"5878","article-title":"pathGCN: Learning general graph spatial operators from paths","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Eliasof"},{"key":"ref57","first-page":"1","article-title":"Ordered GNN: Ordering message passing to deal with heterophily and over-smoothing","volume-title":"Proc. 11th Int. Conf. Learn. Representations","author":"Song"},{"key":"ref58","first-page":"9224","article-title":"Improving graph neural networks with learnable propagation operators","volume-title":"Proc. 40th Int. Conf. Mach. Learn.","author":"Eliasof"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33013959"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401134"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467417"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3209154"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuron.2020.06.014"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403088"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3411903"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2022.3142179"},{"key":"ref67","first-page":"1","article-title":"A learning-based iterative method for solving vehicle routing problems","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Lu"},{"key":"ref68","first-page":"6412","article-title":"Graph convolutional policy network for goal-directed molecular graph generation","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"You"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-economics-083120-111540"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.3386\/w16177"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.3846\/tede.2019.8740"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.2307\/2601198"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1016\/j.jfineco.2011.07.003"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2019.00070"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1145\/2339530.2339670"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220040"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939747"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2017\/299"},{"key":"ref79","first-page":"29628","article-title":"GADBench: Revisiting and benchmarking supervised graph anomaly detection","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Tang"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3357820"},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449989"},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512195"},{"key":"ref83","first-page":"21076","article-title":"Rethinking graph neural networks for anomaly detection","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Tang"}],"container-title":["IEEE Transactions on Knowledge and Data Engineering"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/69\/10709365\/10549866.pdf?arnumber=10549866","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T06:15:38Z","timestamp":1728454538000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10549866\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11]]},"references-count":83,"journal-issue":{"issue":"11"},"URL":"https:\/\/doi.org\/10.1109\/tkde.2024.3409869","relation":{},"ISSN":["1041-4347","1558-2191","2326-3865"],"issn-type":[{"value":"1041-4347","type":"print"},{"value":"1558-2191","type":"electronic"},{"value":"2326-3865","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11]]}}}