{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T05:47:55Z","timestamp":1767332875506,"version":"3.48.0"},"reference-count":56,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":["62572314"],"award-info":[{"award-number":["62572314"]}],"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":["62471301"],"award-info":[{"award-number":["62471301"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Hong Kong Research Grants Council (RGC) Project","award":["PolyU25210821"],"award-info":[{"award-number":["PolyU25210821"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans.Inform.Forensic Secur."],"published-print":{"date-parts":[[2026]]},"DOI":"10.1109\/tifs.2025.3641816","type":"journal-article","created":{"date-parts":[[2025,12,23]],"date-time":"2025-12-23T18:30:25Z","timestamp":1766514625000},"page":"446-459","source":"Crossref","is-referenced-by-count":0,"title":["Revisiting Adversarial Robustness of GNNs Against Structural Attacks: A Simple and Fast Approach"],"prefix":"10.1109","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7691-9943","authenticated-orcid":false,"given":"Xing","family":"Ai","sequence":"first","affiliation":[{"name":"Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1231-1386","authenticated-orcid":false,"given":"Yulin","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Hong Kong Chu Hai College, Tuen Mun, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5816-4126","authenticated-orcid":false,"given":"Yu","family":"Zheng","sequence":"additional","affiliation":[{"name":"University of California, Irvine, Irvine, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3913-5001","authenticated-orcid":false,"given":"Gaolei","family":"Li","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6831-3973","authenticated-orcid":false,"given":"Jianhua","family":"Li","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1383-2765","authenticated-orcid":false,"given":"Kai","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-023-01949-9"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TCSS.2023.3302651"},{"key":"ref3","first-page":"24661","article-title":"MuSe-GNN: Learning unified gene representation from multimodal biological graph data","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Liu"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/550"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE53745.2022.00006"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-032-06066-2_10"},{"key":"ref7","first-page":"1","article-title":"Topological adversarial attacks on graph neural networks via projected meta learning","volume-title":"Proc. IEEE Int. Conf. Evolving Adapt. Intell. Syst. (EAIS)","author":"Aburidi"},{"key":"ref8","first-page":"27966","article-title":"Towards reasonable budget allocation in untargeted graph structure attacks via gradient debias","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Liu"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3411903"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219826"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403049"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1145\/3336191.3371789"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1145\/3437963.3441735"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2023.3322129"},{"key":"ref15","first-page":"11313","article-title":"Semi-supervised classification with graph convolutional networks","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Kipf"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539484"},{"key":"ref17","first-page":"22118","article-title":"Open graph benchmark: Datasets for machine learning on graphs","volume-title":"Proc. Conf. Neural Inf. Process. Syst. (NeurIPS)","volume":"33","author":"Hu"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.aiopen.2021.01.001"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2023.127229"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.109042"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1145\/3539597.3570407"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.118737"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1145\/3584945"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1145\/3511808.3557356"},{"key":"ref25","first-page":"20","article-title":"Graph attention networks","volume":"1050","author":"Veli\u010dkovi\u0107","year":"2018","journal-title":"Stat"},{"key":"ref26","first-page":"1024","article-title":"Inductive representation learning on large graphs","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Hamilton"},{"journal-title":"arXiv:1810.00826","article-title":"How powerful are graph neural networks?","author":"Xu","key":"ref27"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512179"},{"key":"ref29","article-title":"Black-box attacks against signed graph analysis via balance poisoning","author":"Zhou","year":"2023","journal-title":"arXiv:2309.02396"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2024.3364366"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2023.3327876"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1145\/3460120.3485387"},{"key":"ref33","first-page":"9263","article-title":"GNNGuard: Defending graph neural networks against adversarial attacks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Zhang"},{"key":"ref34","first-page":"3338","article-title":"Adversarial robustness in graph neural networks: A Hamiltonian energy conservation approach","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Zhao"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v39i12.33471"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i19.30098"},{"key":"ref37","first-page":"4694","article-title":"EvenNet: Ignoring odd-hop neighbors improves robustness of graph neural networks","volume-title":"Proc. 36th Int. Conf. Neural Inf. Process. Syst.","author":"Lei"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.26421\/QIC12.5-6-4"},{"key":"ref39","first-page":"6861","article-title":"Simplifying graph convolutional networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Wu"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/tkde.2025.3586369"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1145\/3437963.3441734"},{"key":"ref42","article-title":"Representation learning with contrastive predictive coding","author":"van den Oord","year":"2018","journal-title":"arXiv:1807.03748"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1023\/A:1009953814988"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1145\/276675.276685"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1609\/aimag.v29i3.2157"},{"key":"ref46","article-title":"Multi-scale attributed node embedding","author":"Rozemberczki","year":"2019","journal-title":"arXiv:1909.13021"},{"key":"ref47","first-page":"1725","article-title":"Simple and deep graph convolutional networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Chen"},{"journal-title":"arXiv:1810.05997","article-title":"Predict then propagate: Graph neural networks meet personalized PageRank","author":"Gasteiger","key":"ref48"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330851"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1145\/3721146.3721949"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2024.3403925"},{"key":"ref52","first-page":"30789","article-title":"Graph adversarial diffusion convolution","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Liu"},{"key":"ref53","article-title":"DeepRobust: A PyTorch library for adversarial attacks and defenses","author":"Li","year":"2020","journal-title":"arXiv:2005.06149"},{"key":"ref54","first-page":"249","article-title":"Understanding the difficulty of training deep feedforward neural networks","volume-title":"Proc. 13th Int. Conf. Artif. Intell. Statist.","author":"Glorot"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref56","first-page":"7637","article-title":"Robustness of graph neural networks at scale","volume-title":"Proc. Neural Inf. Process. Syst.","volume":"34","author":"Geisler"}],"container-title":["IEEE Transactions on Information Forensics and Security"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/10206\/11313711\/11311611.pdf?arnumber=11311611","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T05:45:27Z","timestamp":1767332727000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11311611\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":56,"URL":"https:\/\/doi.org\/10.1109\/tifs.2025.3641816","relation":{},"ISSN":["1556-6013","1556-6021"],"issn-type":[{"type":"print","value":"1556-6013"},{"type":"electronic","value":"1556-6021"}],"subject":[],"published":{"date-parts":[[2026]]}}}