{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T21:41:58Z","timestamp":1774042918017,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":47,"publisher":"ACM","license":[{"start":{"date-parts":[[2026,11,10]],"date-time":"2026-11-10T00:00:00Z","timestamp":1794268800000},"content-version":"vor","delay-in-days":365,"URL":"http:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000871","name":"Mayo Clinic","doi-asserted-by":"publisher","award":["R01AG084236, R01AG083039"],"award-info":[{"award-number":["R01AG084236, R01AG083039"]}],"id":[{"id":"10.13039\/100000871","id-type":"DOI","asserted-by":"publisher"}]},{"name":"U.S. National Science Foundation","award":["2317117"],"award-info":[{"award-number":["2317117"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,11,10]]},"DOI":"10.1145\/3746252.3761282","type":"proceedings-article","created":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T23:59:18Z","timestamp":1762559958000},"page":"1035-1045","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Leveraging Vulnerabilities in Temporal Graph Neural Networks via Strategic High-Impact Assaults"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-9046-0470","authenticated-orcid":false,"given":"Dong Hyun","family":"Jeon","sequence":"first","affiliation":[{"name":"Bowling Green State University, Bowling Green, Ohio, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-7107-2880","authenticated-orcid":false,"given":"Lijing","family":"Zhu","sequence":"additional","affiliation":[{"name":"University of Houston - Clear Lake, Houston, Texas, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-1098-4023","authenticated-orcid":false,"given":"Haifang","family":"Li","sequence":"additional","affiliation":[{"name":"Mayo Clinic, Jacksonville, Florida, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7015-0491","authenticated-orcid":false,"given":"Pengze","family":"Li","sequence":"additional","affiliation":[{"name":"Mayo Clinic, Jacksonville, Florida, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0434-2513","authenticated-orcid":false,"given":"Jingna","family":"Feng","sequence":"additional","affiliation":[{"name":"Mayo Clinic, Jacksonville, Florida, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4323-642X","authenticated-orcid":false,"given":"Tiehang","family":"Duan","sequence":"additional","affiliation":[{"name":"Grand Valley State University, Allendale, Michigan, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2631-9223","authenticated-orcid":false,"given":"Houbing Herbert","family":"Song","sequence":"additional","affiliation":[{"name":"University of Maryland at Baltimore County, Baltimore, Maryland, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4267-1924","authenticated-orcid":false,"given":"Cui","family":"Tao","sequence":"additional","affiliation":[{"name":"Mayo Clinic, Jacksonville, Florida, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1069-9236","authenticated-orcid":false,"given":"Shuteng","family":"Niu","sequence":"additional","affiliation":[{"name":"Mayo Clinic, Jacksonville, Florida, USA"}]}],"member":"320","published-online":{"date-parts":[[2025,11,10]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/3469379.3469386"},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/1134271.1134280"},{"key":"e_1_3_2_2_3_1","volume-title":"Fast Gradient Attack on Network Embedding. arXiv preprint arXiv:1809.02797","author":"Chen Jinyin","year":"2018","unstructured":"Jinyin Chen, Yangyang Wu, Xuanheng Xu, Yixian Chen, Haibin Zheng, and Qi Xuan. 2018. Fast Gradient Attack on Network Embedding. arXiv preprint arXiv:1809.02797 (2018)."},{"key":"e_1_3_2_2_4_1","first-page":"2091","article-title":"Time-Aware Gradient Attack on Dynamic Network Link Prediction","volume":"35","author":"Chen Jinyin","year":"2021","unstructured":"Jinyin Chen, Jian Zhang, Zhi Chen, Min Du, and Qi Xuan. 2021. Time-Aware Gradient Attack on Dynamic Network Link Prediction. IEEE Transactions on Knowledge and Data Engineering, Vol. 35, 2 (2021), 2091-2102.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_2_2_5_1","volume-title":"Proceedings of the International Conference on Machine Learning. PMLR, 1115-1124","author":"Dai Hanjun","year":"2018","unstructured":"Hanjun Dai, Hui Li, Tian Tian, Xin Huang, Lin Wang, Jun Zhu, and Le Song. 2018. Adversarial Attack on Graph Structured Data. In Proceedings of the International Conference on Machine Learning. PMLR, 1115-1124."},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1214\/18-AOAS1176"},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313488"},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467145"},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3465336.3475110"},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/BigData62323.2024.10825929"},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447556.3447566"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330895"},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i12.29239"},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/1830252.1830262"},{"key":"e_1_3_2_2_15_1","first-page":"82","article-title":"Adversarial attack on large scale graph","volume":"35","author":"Li Jintang","year":"2021","unstructured":"Jintang Li, Tao Xie, Liang Chen, Fenfang Xie, Xiangnan He, and Zibin Zheng. 2021. Adversarial attack on large scale graph. IEEE Transactions on Knowledge and Data Engineering, Vol. 35, 1 (2021), 82-95.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP43922.2022.9747850"},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/956863.956972"},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3252175"},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.3390\/electronics11152287"},{"key":"e_1_3_2_2_20_1","first-page":"4756","article-title":"Towards More Practical Adversarial Attacks on Graph Neural Networks","volume":"33","author":"Ma Jiaqi","year":"2020","unstructured":"Jiaqi Ma, Shuangrui Ding, and Qiaozhu Mei. 2020. Towards More Practical Adversarial Attacks on Graph Neural Networks. Advances in Neural Information Processing Systems, Vol. 33 (2020), 4756-4766.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/TETCI.2022.3222545"},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.5555\/1543767.1543769"},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5984"},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3341161.3343519"},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2020.113303"},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3208351"},{"key":"e_1_3_2_2_27_1","volume-title":"Temporal Graph Networks for Deep Learning on Dynamic Graphs. arXiv preprint arXiv:2006.10637","author":"Rossi Emanuele","year":"2020","unstructured":"Emanuele Rossi, Ben Chamberlain, Fabrizio Frasca, Davide Eynard, Federico Monti, and Michael Bronstein. 2020. Temporal Graph Networks for Deep Learning on Dynamic Graphs. arXiv preprint arXiv:2006.10637 (2020)."},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3336191.3371845"},{"key":"e_1_3_2_2_29_1","volume-title":"Centrality measures in complex networks: A survey. arXiv preprint arXiv:2011.07190","author":"Saxena Akrati","year":"2020","unstructured":"Akrati Saxena and Sudarshan Iyengar. 2020. Centrality measures in complex networks: A survey. arXiv preprint arXiv:2011.07190 (2020)."},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599517"},{"key":"e_1_3_2_2_31_1","first-page":"7693","article-title":"Adversarial Attack and Defense on Graph Data: A Survey","volume":"35","author":"Sun Lichao","year":"2022","unstructured":"Lichao Sun, Yingtong Dou, Carl Yang, Kai Zhang, Ji Wang, S. Yu Philip, Lifang He, and Bo Li. 2022a. Adversarial Attack and Defense on Graph Data: A Survey. IEEE Transactions on Knowledge and Data Engineering, Vol. 35, 8 (2022), 7693-7711.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/IPCCC55026.2022.9894334"},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-41695-z"},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1007\/s41060-019-00189-x"},{"key":"e_1_3_2_2_35_1","volume-title":"Adversarial Examples on Graph Data: Deep Insights into Attack and Defense. arXiv preprint arXiv:1903.01610","author":"Wu Huijun","year":"2019","unstructured":"Huijun Wu, Chen Wang, Yuriy Tyshetskiy, Andrew Docherty, Kai Lu, and Liming Zhu. 2019. Adversarial Examples on Graph Data: Deep Insights into Attack and Defense. arXiv preprint arXiv:1903.01610 (2019)."},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"e_1_3_2_2_37_1","volume-title":"Inductive Representation Learning on Temporal Graphs. arXiv preprint arXiv:2002.07962","author":"Xu Da","year":"2020","unstructured":"Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, and Kannan Achan. 2020. Inductive Representation Learning on Temporal Graphs. arXiv preprint arXiv:2002.07962 (2020)."},{"key":"e_1_3_2_2_38_1","volume-title":"Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective. arXiv preprint arXiv:1906.04214","author":"Xu Kaidi","year":"2019","unstructured":"Kaidi Xu, Hongge Chen, Sijia Liu, Pin-Yu Chen, Tsui-Wei Weng, Mingyi Hong, and Xue Lin. 2019. Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective. arXiv preprint arXiv:1906.04214 (2019)."},{"key":"e_1_3_2_2_39_1","volume-title":"Proceedings of the 2017 Systems and Information Engineering Design Symposium (SIEDS). IEEE, 112-116","author":"Zeager Mary Frances","unstructured":"Mary Frances Zeager, Aksheetha Sridhar, Nathan Fogal, Stephen Adams, Donald E. Brown, and Peter A. Beling. 2017. Adversarial Learning in Credit Card Fraud Detection. In Proceedings of the 2017 Systems and Information Engineering Design Symposium (SIEDS). IEEE, 112-116."},{"key":"e_1_3_2_2_40_1","first-page":"9263","article-title":"GNNGuard: Defending Graph Neural Networks Against Adversarial Attacks","volume":"33","author":"Zhang Xiang","year":"2020","unstructured":"Xiang Zhang and Marinka Zitnik. 2020. GNNGuard: Defending Graph Neural Networks Against Adversarial Attacks. Advances in Neural Information Processing Systems, Vol. 33 (2020), 9263-9275.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICA63002.2024.00028"},{"key":"e_1_3_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2024.3438238"},{"key":"e_1_3_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330851"},{"key":"e_1_3_2_2_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/BigData62323.2024.10825244"},{"key":"e_1_3_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467314"},{"key":"e_1_3_2_2_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220078"},{"key":"e_1_3_2_2_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394520"}],"event":{"name":"CIKM '25: The 34th ACM International Conference on Information and Knowledge Management","location":"Seoul Republic of Korea","acronym":"CIKM '25","sponsor":["SIGIR ACM Special Interest Group on Information Retrieval","SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"]},"container-title":["Proceedings of the 34th ACM International Conference on Information and Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/abs\/10.1145\/3746252.3761282","content-type":"text\/html","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3746252.3761282","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3746252.3761282","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T00:26:02Z","timestamp":1765499162000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3746252.3761282"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,10]]},"references-count":47,"alternative-id":["10.1145\/3746252.3761282","10.1145\/3746252"],"URL":"https:\/\/doi.org\/10.1145\/3746252.3761282","relation":{},"subject":[],"published":{"date-parts":[[2025,11,10]]},"assertion":[{"value":"2025-11-10","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}