{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T05:42:25Z","timestamp":1777873345705,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":52,"publisher":"ACM","funder":[{"DOI":"10.13039\/501100006374","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62402106, 62372334"],"award-info":[{"award-number":["62402106, 62372334"]}],"id":[{"id":"10.13039\/501100006374","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of Jiangsu Province of China","award":["BK20241272"],"award-info":[{"award-number":["BK20241272"]}]},{"DOI":"10.13039\/501100006374","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2242025K30025"],"award-info":[{"award-number":["2242025K30025"]}],"id":[{"id":"10.13039\/501100006374","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Start-Up Research Fund of Southeast University","award":["RF1028623129"],"award-info":[{"award-number":["RF1028623129"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,8,3]]},"DOI":"10.1145\/3711896.3737091","type":"proceedings-article","created":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T13:30:13Z","timestamp":1754055013000},"page":"1049-1060","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Prompt as a Double-Edged Sword: A Dynamic Equilibrium Gradient-Assigned Attack against Graph Prompt Learning"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6894-1331","authenticated-orcid":false,"given":"Ju","family":"Jia","sequence":"first","affiliation":[{"name":"School of Cyber Science and Engineering, Southeast University, NanJing, China and Engineering Research Center of Blockchain Application, Supervision and Management (Southeast University), Ministry of Education, NanJing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-2449-5171","authenticated-orcid":false,"given":"Jingxuan","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Engineering, Southeast University, NanJing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4753-8161","authenticated-orcid":false,"given":"Di","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0930-0283","authenticated-orcid":false,"given":"Cong","family":"Wu","sequence":"additional","affiliation":[{"name":"University of Hong Kong, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-7933-4398","authenticated-orcid":false,"given":"Hengjie","family":"Zhu","sequence":"additional","affiliation":[{"name":"University of the Chinese Academy of Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8085-1312","authenticated-orcid":false,"given":"Lina","family":"Wang","sequence":"additional","affiliation":[{"name":"Wuhan University, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,8,3]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.109631"},{"key":"e_1_3_2_2_2_1","volume-title":"Understanding and improving graph injection attack by promoting unnoticeability. arXiv preprint arXiv:2202.08057","author":"Chen Yongqiang","year":"2022","unstructured":"Yongqiang Chen, Han Yang, Yonggang Zhang, Kaili Ma, Tongliang Liu, Bo Han, and James Cheng. 2022. Understanding and improving graph injection attack by promoting unnoticeability. arXiv preprint arXiv:2202.08057 (2022)."},{"key":"e_1_3_2_2_3_1","volume-title":"Universal prompt tuning for graph neural networks. Advances in Neural Information Processing Systems 36","author":"Fang Taoran","year":"2024","unstructured":"Taoran Fang, Yunchao Zhang, Yang Yang, Chunping Wang, and Lei Chen. 2024. Universal prompt tuning for graph neural networks. Advances in Neural Information Processing Systems 36 (2024)."},{"key":"e_1_3_2_2_4_1","volume-title":"Adaptive Hierarchical Graph Cut for Multi-granularity Out-ofdistribution Detection","author":"Fang Xiang","year":"2025","unstructured":"Xiang Fang, Arvind Easwaran, Blaise Genest, and Ponnuthurai Nagaratnam Suganthan. 2025. Adaptive Hierarchical Graph Cut for Multi-granularity Out-ofdistribution Detection. IEEE Transactions on Artificial Intelligence (2025)."},{"key":"e_1_3_2_2_5_1","first-page":"7637","article-title":"Robustness of graph neural networks at scale","volume":"34","author":"Geisler Simon","year":"2021","unstructured":"Simon Geisler, Tobias Schmidt, Daniel Z\u00fcgner, Aleksandar Bojchevski, and Stephan G\u00fcnnemann. 2021. Robustness of graph neural networks at scale. Advances in Neural Information Processing Systems 34 (2021), 7637-7649.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_6_1","volume-title":"Revisiting robustness in graph machine learning. arXiv preprint arXiv:2305.00851","author":"Gosch Lukas","year":"2023","unstructured":"Lukas Gosch, Daniel Sturm, Simon Geisler, and Stephan G\u00fcnnemann. 2023. Revisiting robustness in graph machine learning. arXiv preprint arXiv:2305.00851 (2023)."},{"key":"e_1_3_2_2_7_1","volume-title":"Learning deep representations by mutual information estimation and maximization. arXiv preprint arXiv:1808.06670","author":"Hjelm R Devon","year":"2018","unstructured":"R Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Phil Bachman, Adam Trischler, and Yoshua Bengio. 2018. Learning deep representations by mutual information estimation and maximization. arXiv preprint arXiv:1808.06670 (2018)."},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539321"},{"key":"e_1_3_2_2_9_1","volume-title":"International conference on machine learning. PMLR, 2790-2799","author":"Houlsby Neil","year":"2019","unstructured":"Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly. 2019. Parameter-efficient transfer learning for NLP. In International conference on machine learning. PMLR, 2790-2799."},{"key":"e_1_3_2_2_10_1","volume-title":"SIGFinger: A Subtle and Interactive GNN Fingerprinting Scheme via Spatial Structure Inference Perturbation","author":"Jia Ju","year":"2025","unstructured":"Ju Jia, Renjie Li, CongWu, Siqi Ma, LinaWang, and Robert H Deng. 2025. SIGFinger: A Subtle and Interactive GNN Fingerprinting Scheme via Spatial Structure Inference Perturbation. IEEE Transactions on Dependable and Secure Computing (2025)."},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2022.3168029"},{"key":"e_1_3_2_2_12_1","first-page":"5665","article-title":"Consensus-clusteringbased automatic distribution matching for cross-domain image steganalysis","volume":"35","author":"Jia Ju","year":"2022","unstructured":"Ju Jia, Meng Luo, Siqi Ma, Lina Wang, and Yang Liu. 2022. Consensus-clusteringbased automatic distribution matching for cross-domain image steganalysis. IEEE Transactions on Knowledge and Data Engineering 35, 6 (2022), 5665-5679.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_2_2_13_1","volume-title":"A Causality- Aligned Structure Rationalization Scheme Against Adversarial Biased Perturbations for Graph Neural Networks","author":"Jia Ju","year":"2023","unstructured":"Ju Jia, Siqi Ma, Yang Liu, Lina Wang, and Robert H Deng. 2023. A Causality- Aligned Structure Rationalization Scheme Against Adversarial Biased Perturbations for Graph Neural Networks. IEEE Transactions on Information Forensics and Security (2023)."},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.2995070"},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2019.107105"},{"key":"e_1_3_2_2_16_1","volume-title":"Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907","author":"Kipf Thomas N","year":"2016","unstructured":"Thomas N Kipf and MaxWelling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)."},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3696410.3714555"},{"key":"e_1_3_2_2_18_1","volume-title":"The power of scale for parameter-efficient prompt tuning. arXiv preprint arXiv:2104.08691","author":"Lester Brian","year":"2021","unstructured":"Brian Lester, Rami Al-Rfou, and Noah Constant. 2021. The power of scale for parameter-efficient prompt tuning. arXiv preprint arXiv:2104.08691 (2021)."},{"key":"e_1_3_2_2_19_1","volume-title":"The Eleventh International Conference on Learning Representations.","author":"Li Kuan","year":"2023","unstructured":"Kuan Li, Yang Liu, Xiang Ao, and Qing He. 2023. Revisiting graph adversarial attack and defense from a data distribution perspective. In The Eleventh International Conference on Learning Representations."},{"key":"e_1_3_2_2_20_1","volume-title":"Prefix-tuning: Optimizing continuous prompts for generation. arXiv preprint arXiv:2101.00190","author":"Li Xiang Lisa","year":"2021","unstructured":"Xiang Lisa Li and Percy Liang. 2021. Prefix-tuning: Optimizing continuous prompts for generation. arXiv preprint arXiv:2101.00190 (2021)."},{"key":"e_1_3_2_2_21_1","volume-title":"Fairness-aware Prompt Tuning for Graph Neural Networks. In THE WEB CONFERENCE","author":"Li Zhengpin","year":"2025","unstructured":"Zhengpin Li, Minhua Lin, Jian Wang, and Suhang Wang. [n. d.]. Fairness-aware Prompt Tuning for Graph Neural Networks. In THE WEB CONFERENCE 2025."},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.109042"},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3511808.3557238"},{"key":"e_1_3_2_2_24_1","volume-title":"Towards reasonable budget allocation in untargeted graph structure attacks via gradient debias. arXiv preprint arXiv:2304.00010","author":"Liu Zihan","year":"2023","unstructured":"Zihan Liu, Yun Luo, Lirong Wu, Zicheng Liu, and Stan Z Li. 2023. Towards reasonable budget allocation in untargeted graph structure attacks via gradient debias. arXiv preprint arXiv:2304.00010 (2023)."},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583386"},{"key":"e_1_3_2_2_26_1","first-page":"8954","article-title":"Are defenses for graph neural networks robust","volume":"35","author":"Mujkanovic Felix","year":"2022","unstructured":"Felix Mujkanovic, Simon Geisler, Stephan G\u00fcnnemann, and Aleksandar Bojchevski. 2022. Are defenses for graph neural networks robust? Advances in Neural Information Processing Systems 35 (2022), 8954-8968.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_27_1","volume-title":"A survey on oversmoothing in graph neural networks. arXiv preprint arXiv:2303.10993","author":"Rusch T Konstantin","year":"2023","unstructured":"T Konstantin Rusch, MichaelMBronstein, and Siddhartha Mishra. 2023. A survey on oversmoothing in graph neural networks. arXiv preprint arXiv:2303.10993 (2023)."},{"key":"e_1_3_2_2_28_1","volume-title":"Pitfalls of graph neural network evaluation. arXiv preprint arXiv:1811.05868","author":"Shchur Oleksandr","year":"2018","unstructured":"Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski, and Stephan G\u00fcnnemann. 2018. Pitfalls of graph neural network evaluation. arXiv preprint arXiv:1811.05868 (2018)."},{"key":"e_1_3_2_2_29_1","volume-title":"Masked label prediction: Unified message passing model for semisupervised classification. arXiv preprint arXiv:2009.03509","author":"Shi Yunsheng","year":"2020","unstructured":"Yunsheng Shi, Zhengjie Huang, Shikun Feng, Hui Zhong, Wenjin Wang, and Yu Sun. 2020. Masked label prediction: Unified message passing model for semisupervised classification. arXiv preprint arXiv:2009.03509 (2020)."},{"key":"e_1_3_2_2_30_1","volume-title":"Krait: A Backdoor Attack Against Graph Prompt Tuning. arXiv preprint arXiv:2407.13068","author":"Song Ying","year":"2024","unstructured":"Ying Song, Rita Singh, and Balaji Palanisamy. 2024. Krait: A Backdoor Attack Against Graph Prompt Tuning. arXiv preprint arXiv:2407.13068 (2024)."},{"key":"e_1_3_2_2_31_1","volume-title":"Infograph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization. arXiv preprint arXiv:1908.01000","author":"Sun Fan-Yun","year":"2019","unstructured":"Fan-Yun Sun, Jordan Hoffmann, Vikas Verma, and Jian Tang. 2019. Infograph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization. arXiv preprint arXiv:1908.01000 (2019)."},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539249"},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599256"},{"key":"e_1_3_2_2_34_1","first-page":"15920","article-title":"Adversarial graph augmentation to improve graph contrastive learning","volume":"34","author":"Suresh Susheel","year":"2021","unstructured":"Susheel Suresh, Pan Li, Cong Hao, and Jennifer Neville. 2021. Adversarial graph augmentation to improve graph contrastive learning. Advances in Neural Information Processing Systems 34 (2021), 15920-15933.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_35_1","volume-title":"ICLR 2021 Workshop on Geometrical and Topological Representation Learning.","author":"Thakoor Shantanu","year":"2021","unstructured":"Shantanu Thakoor, Corentin Tallec, Mohammad Gheshlaghi Azar, R\u00e9mi Munos, Petar Velickovic, and Michal Valko. 2021. Bootstrapped representation learning on graphs. In ICLR 2021 Workshop on Geometrical and Topological Representation Learning."},{"key":"e_1_3_2_2_36_1","volume-title":"Graph attention networks. arXiv preprint arXiv:1710.10903","author":"Velickovic Petar","year":"2017","unstructured":"Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)."},{"key":"e_1_3_2_2_37_1","volume-title":"Deep graph infomax. arXiv preprint arXiv:1809.10341","author":"Velickovic Petar","year":"2018","unstructured":"Petar Velickovic, William Fedus, William L Hamilton, Pietro Li\u00f2, Yoshua Bengio, and R Devon Hjelm. 2018. Deep graph infomax. arXiv preprint arXiv:1809.10341 (2018)."},{"key":"e_1_3_2_2_38_1","volume-title":"Does Graph Prompt Work? A Data Operation Perspective with Theoretical Analysis. arXiv preprint arXiv:2410.01635","author":"Wang Qunzhong","year":"2024","unstructured":"Qunzhong Wang, Xiangguo Sun, and Hong Cheng. 2024. Does Graph Prompt Work? A Data Operation Perspective with Theoretical Analysis. arXiv preprint arXiv:2410.01635 (2024). https:\/\/arxiv.org\/abs\/2410.01635"},{"key":"e_1_3_2_2_39_1","volume-title":"Subgraph Pooling: Tackling Negative Transfer on Graphs. IJCAI.","author":"Wang Zehong","year":"2024","unstructured":"Zehong Wang, Zheyuan Zhang, Chuxu Zhang, and Yanfang Ye. 2024. Subgraph Pooling: Tackling Negative Transfer on Graphs. IJCAI."},{"key":"e_1_3_2_2_40_1","volume-title":"Adversarial examples on graph data: Deep insights into attack and defense. arXiv preprint arXiv:1903.01610","author":"Tyshetskiy Yuriy","year":"2019","unstructured":"HuijunWu, ChenWang, 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_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512156"},{"key":"e_1_3_2_2_42_1","volume-title":"International conference on machine learning. PMLR, 40-48","author":"Yang Zhilin","year":"2016","unstructured":"Zhilin Yang, William Cohen, and Ruslan Salakhudinov. 2016. Revisiting semisupervised learning with graph embeddings. In International conference on machine learning. PMLR, 40-48."},{"key":"e_1_3_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/3627673.3679686"},{"key":"e_1_3_2_2_44_1","volume-title":"Graph contrastive learning with augmentations. Advances in neural information processing systems 33","author":"You Yuning","year":"2020","unstructured":"Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen. 2020. Graph contrastive learning with augmentations. Advances in neural information processing systems 33 (2020), 5812-5823."},{"key":"e_1_3_2_2_45_1","volume-title":"Generalized graph prompt: Toward a unification of pre-training and downstream tasks on graphs","author":"Yu Xingtong","year":"2024","unstructured":"Xingtong Yu, Zhenghao Liu, Yuan Fang, Zemin Liu, Sihong Chen, and Xinming Zhang. 2024. Generalized graph prompt: Toward a unification of pre-training and downstream tasks on graphs. IEEE Transactions on Knowledge and Data Engineering (2024)."},{"key":"e_1_3_2_2_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/3589334.3645423"},{"key":"e_1_3_2_2_47_1","volume-title":"Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models. arXiv preprint arXiv:2106.10199","author":"Zaken Elad Ben","year":"2021","unstructured":"Elad Ben Zaken, Shauli Ravfogel, and Yoav Goldberg. 2021. Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models. arXiv preprint arXiv:2106.10199 (2021)."},{"key":"e_1_3_2_2_48_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i9.26336"},{"key":"e_1_3_2_2_49_1","volume-title":"Factual probing is [mask]: Learning vs. learning to recall. arXiv preprint arXiv:2104.05240","author":"Zhong Zexuan","year":"2021","unstructured":"Zexuan Zhong, Dan Friedman, and Danqi Chen. 2021. Factual probing is [mask]: Learning vs. learning to recall. arXiv preprint arXiv:2104.05240 (2021)."},{"key":"e_1_3_2_2_50_1","volume-title":"ProG: A Graph Prompt Learning Benchmark. the Thirty-Eighth Advances in Neural Information Processing Systems (NeurIPS 2024)","author":"Zi Chenyi","year":"2024","unstructured":"Chenyi Zi, Haihong Zhao, Xiangguo Sun, Yiqing Lin, Hong Cheng, and Jia Li. 2024. ProG: A Graph Prompt Learning Benchmark. the Thirty-Eighth Advances in Neural Information Processing Systems (NeurIPS 2024) (2024)."},{"key":"e_1_3_2_2_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220078"},{"key":"e_1_3_2_2_52_1","unstructured":"Daniel Z\u00fcgner and Stephan G\u00fcnnemann. 2024. Adversarial Attacks on Graph Neural Networks via Meta Learning. arXiv:1902.08412 [cs.LG] https:\/\/arxiv.org\/abs\/1902.08412"}],"event":{"name":"KDD '25: The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Toronto ON Canada","acronym":"KDD '25","sponsor":["SIGKDD ACM Special Interest Group on Knowledge Discovery in Data","SIGMOD ACM Special Interest Group on Management of Data"]},"container-title":["Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3711896.3737091","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T17:54:38Z","timestamp":1777571678000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3711896.3737091"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,3]]},"references-count":52,"alternative-id":["10.1145\/3711896.3737091","10.1145\/3711896"],"URL":"https:\/\/doi.org\/10.1145\/3711896.3737091","relation":{},"subject":[],"published":{"date-parts":[[2025,8,3]]},"assertion":[{"value":"2025-08-03","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}