{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T15:00:27Z","timestamp":1765292427614,"version":"3.46.0"},"publisher-location":"Singapore","reference-count":38,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819698486"},{"type":"electronic","value":"9789819698493"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-981-96-9849-3_3","type":"book-chapter","created":{"date-parts":[[2025,7,18]],"date-time":"2025-07-18T08:59:21Z","timestamp":1752829161000},"page":"27-43","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["GNNHacker: Adaptive Subgraph Backdoor Attacking Method with Saliency Analysis and Joint Optimization"],"prefix":"10.1007","author":[{"given":"Xiaojie","family":"Wu","sequence":"first","affiliation":[]},{"given":"Yan","family":"Luo","sequence":"additional","affiliation":[]},{"given":"Shujie","family":"Li","sequence":"additional","affiliation":[]},{"given":"Xiping","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Qiang","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Chengming","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,7,19]]},"reference":[{"key":"3_CR1","doi-asserted-by":"crossref","unstructured":"Yang, S.Q., et al.: Variational co-embedding learning for attributed network clustering. Knowl.-Based Syst. 270, 110530 (2023)","DOI":"10.1016\/j.knosys.2023.110530"},{"issue":"5","key":"3_CR2","first-page":"755","volume":"43","author":"BB Xu","year":"2020","unstructured":"Xu, B.B., Cen, K.Y., Huang, J.J., et al.: A survey on graph convolutional neural network. Chin. J. Comput. 43(5), 755\u2013780 (2020)","journal-title":"Chin. J. Comput."},{"key":"3_CR3","doi-asserted-by":"crossref","unstructured":"Yang, S.Q., Doan, B.G., Montague, P., et al.: Transferable graph backdoor attack. In: Proceedings of the 25th International Symposium on Research in Attacks, Intrusions and Defenses, pp. 321\u2013332 (2022)","DOI":"10.1145\/3545948.3545976"},{"key":"3_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2023.119611","volume":"649","author":"SC Tao","year":"2023","unstructured":"Tao, S.C., Cao, Q., Shen, H.W., et al.: Adversarial camouflage for node injection attack on graphs. Inf. Sci. 649, 119611 (2023)","journal-title":"Inf. Sci."},{"key":"3_CR5","unstructured":"Ju, M.X., Fan, Y.J., Ye, Y.F., et al.: Black-box node injection attack for graph neural networks. arXiv preprint arXiv:2202.09389 (2022)"},{"key":"3_CR6","doi-asserted-by":"crossref","unstructured":"Mu, J.M., Wang, B.H., Li, I., et al.: A hard label black-box adversarial attack against graph neural networks. In: Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security, pp. 108\u2013125 (2021)","DOI":"10.1145\/3460120.3484796"},{"key":"3_CR7","unstructured":"Shao, J.L., Wang, Y.Q., Shi, B.S., et al.: Adversarial for social privacy: A poisoning strategy to degrade user identity linkage. arXiv preprint arXiv:2209.00269 (2022)"},{"key":"3_CR8","doi-asserted-by":"crossref","unstructured":"Zang, X., Xie, Y., Chen, J., et al.: Graph universal adversarial attacks: a few bad actors ruin graph learning models. In: Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, pp. 3328\u20133334 (2021)","DOI":"10.24963\/ijcai.2021\/458"},{"key":"3_CR9","doi-asserted-by":"crossref","unstructured":"Liu, Z.H., Luo, Y., Wu, L.R., et al.: Are gradients on graph structure reliable in gray-box attacks? In: Proceedings of the 31st ACM International Conference on Information and Knowledge Management, pp. 1360\u20131368 (2022)","DOI":"10.1145\/3511808.3557238"},{"key":"3_CR10","doi-asserted-by":"crossref","unstructured":"Ma, Y., Wang, S.H., Derr, T., et al.: Graph adversarial attack via re-wiring. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 1161\u20131169 (2021)","DOI":"10.1145\/3447548.3467416"},{"issue":"1","key":"3_CR11","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1109\/TNNLS.2022.3182979","volume":"35","author":"YM Li","year":"2022","unstructured":"Li, Y.M., Jiang, Y., Li, Z.F., et al.: Backdoor learning: a survey. IEEE Trans. Neural Netw. Learn. Syst. 35(1), 5\u201322 (2022)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"3_CR12","doi-asserted-by":"crossref","unstructured":"Tan, T.L., Shokri, R.: Bypassing backdoor detection algorithms in deep learning. In: 2020 IEEE European Symposium on Security and Privacy (EuroS&P), pp. 175\u2013183 (2020)","DOI":"10.1109\/EuroSP48549.2020.00019"},{"issue":"11","key":"3_CR13","first-page":"2364","volume":"58","author":"DW Chen","year":"2021","unstructured":"Chen, D.W., Fu, A.M., Zhou, C.Y., et al.: Federated learning backdoor attack scheme based on generative adversarial network. Chin. Acad. Sci. 58(11), 2364\u20132373 (2021)","journal-title":"Chin. Acad. Sci."},{"issue":"8","key":"3_CR14","first-page":"7693","volume":"35","author":"LC Sun","year":"2023","unstructured":"Sun, L.C., Dou, Y.T., Yang, C., et al.: Adversarial attack and defense on graph data: a survey. IEEE Trans. Knowl. Data Eng. 35(8), 7693\u20137711 (2023)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"3_CR15","doi-asserted-by":"crossref","unstructured":"Zhang, Z.X., Jia, J.Y., Wang, B.H., et al.: Backdoor attacks to graph neural networks. In: Proceedings of the 26th ACM Symposium on Access Control Models and Technologies, pp. 15\u201326 (2021)","DOI":"10.1145\/3450569.3463560"},{"issue":"2","key":"3_CR16","doi-asserted-by":"publisher","first-page":"1813","DOI":"10.1109\/TCSS.2023.3260833","volume":"11","author":"HB Zheng","year":"2024","unstructured":"Zheng, H.B., Xiong, H.Y., Ma, H.N., et al.: Link-backdoor backdoor attack on link prediction via node injection. IEEE Trans. Comput. Soc. Syst. 11(2), 1813\u20131816 (2024)","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"key":"3_CR17","doi-asserted-by":"crossref","unstructured":"Xu, J., Xue, M.H., Picek, S.: Explainability-based backdoor attacks against graph neural networks. In: Proceedings of the 3rd ACM Workshop on Wireless Security and Machine Learning, pp. 31\u201336 (2021)","DOI":"10.1145\/3468218.3469046"},{"key":"3_CR18","doi-asserted-by":"crossref","unstructured":"Xu, J., Wang, R., Koffas, S., et al.: More is better (mostly): on the backdoor attacks in federated graph neural networks. In: Proceedings of the 38th Annual Computer Security Applications Conference, pp. 684\u2013698 (2022)","DOI":"10.1145\/3564625.3567999"},{"key":"3_CR19","doi-asserted-by":"crossref","unstructured":"Dai, E., Lin, M., Zhang, X., et al.: Unnoticeable backdoor attacks on graph neural networks. In: Proceedings of the ACM Web Conference, pp. 2263\u20132273 (2023)","DOI":"10.1145\/3543507.3583392"},{"key":"3_CR20","unstructured":"Xi, Z., Pang, R., Ji, S., Wang, T.: Graph backdoor. In: Proceedings of the 30th USENIX Security Symposium (USENIX Security 21), pp. 1523\u20131540 (2021)"},{"key":"3_CR21","unstructured":"Ying, Z.T., Bourgeois, D., You, J.X., et al.: GNNExplainer: generating explanations for graph neural networks. In: Proceedings of the 33rd International Conference on Neural Information Processing Systems, pp. 9244\u20139255 (2019)"},{"issue":"7","key":"3_CR22","doi-asserted-by":"publisher","first-page":"6968","DOI":"10.1109\/TKDE.2022.3187455","volume":"35","author":"Q Huang","year":"2023","unstructured":"Huang, Q., Yamada, M., Tian, Y., et al.: GraphLIME: local interpretable model explanations for graph neural networks. IEEE Trans. Knowl. Data Eng. 35(7), 6968\u20136972 (2023)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"3_CR23","doi-asserted-by":"crossref","unstructured":"Xu, J., Picek, S.: Poster: clean-label backdoor attack on graph neural networks. In: Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security, pp. 3491\u20133493 (2022)","DOI":"10.1145\/3548606.3563531"},{"key":"3_CR24","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Jia, J., Wang, B., Gong, N.Z.: Watermarking graph neural networks based on backdoor attacks. In: Proceedings of the 26th ACM Symposium on Access Control Models and Technologies, pp. 15\u201325. Virtual Event, Spain (2021)","DOI":"10.1145\/3450569.3463560"},{"key":"3_CR25","unstructured":"Tian, Y., Liu, L., Cheng, X., Sun, Y., Wu, J., Hu, Y.: A semantic backdoor attack against graph convolutional networks. arXiv preprint arXiv:2302.14353 (2023)"},{"key":"3_CR26","unstructured":"Zhang, J., Liu, Y., Ma, C., Xu, C., Xia, F.: GUAP: graph universal attack through adversarial patching. In: Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI), pp. 3807\u20133813 (2021)"},{"key":"3_CR27","doi-asserted-by":"crossref","unstructured":"Debnath, A.K., Lopez de Compadre, R.L., Debnath, G., et al.: Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. Correlation with molecular orbital energies and hydrophobicity. J. Med. Chem. 34(2), 786\u2013797 (1991)","DOI":"10.1021\/jm00106a046"},{"issue":"2","key":"3_CR28","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1021\/ci970431+","volume":"38","author":"WR Gillet","year":"1998","unstructured":"Gillet, W.R., Bradshaw, J., Willett, P.: Identification of biological activity profiles using substructural analysis and genetic algorithms. J. Chem. Inf. Comput. Sci. 38(2), 165\u2013179 (1998)","journal-title":"J. Chem. Inf. Comput. Sci."},{"issue":"8","key":"3_CR29","doi-asserted-by":"crossref","first-page":"973","DOI":"10.1093\/bioinformatics\/btg119","volume":"19","author":"S Dobson","year":"2003","unstructured":"Dobson, S., Varley, I.R.: Protein structural classification by class prediction. Bioinformatics 19(8), 973\u2013980 (2003)","journal-title":"Bioinformatics"},{"key":"3_CR30","doi-asserted-by":"crossref","unstructured":"Yanardag, P., Vishwanathan, S.V.N.: Deep graph kernels. In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1365\u20131374 (2015)","DOI":"10.1145\/2783258.2783417"},{"key":"3_CR31","unstructured":"Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Li\u00f2, P., Bengio, Y.: Graph attention networks. In: Proceedings of the International Conference on Learning Representations (ICLR) (2018)"},{"key":"3_CR32","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: Proceedings of the International Conference on Learning Representations (ICLR) (2017)"},{"key":"3_CR33","unstructured":"Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. Adv. Neural Inf. Process. Syst. 30 (2017)"},{"key":"3_CR34","unstructured":"Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks?. arXiv preprint arXiv:1810.00826 (2019). https:\/\/arxiv.org\/abs\/1810.00826"},{"issue":"3","key":"3_CR35","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1089\/big.2020.0062","volume":"8","author":"K Shu","year":"2020","unstructured":"Shu, K., Mahudeswaran, D., Wang, S., Lee, D., Liu, H.: FakeNewsNet: a data repository with news content, social context, and spatiotemporal information for studying fake news on social media. Big Data 8(3), 171\u2013188 (2020)","journal-title":"Big Data"},{"key":"3_CR36","unstructured":"Wei, W., Li, X., Zhang, J., Wu, X.: User preference-aware fake news detection. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI), pp. 4399\u20134405 (2019)"},{"key":"3_CR37","unstructured":"Monti, F., Frasca, F., Eynard, D., Mannion, D., Bronstein, M.M.: Fake news detection on social media using geometric deep learning. In: 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 369\u2013376 (2019)"},{"key":"3_CR38","doi-asserted-by":"crossref","unstructured":"Bian, T., et al.: Rumor detection on social media with bi-directional graph convolutional networks. In: Proceedings of the 34th AAAI Conference on Artificial Intelligence, pp. 549\u2013556 (2020)","DOI":"10.1609\/aaai.v34i01.5393"}],"container-title":["Lecture Notes in Computer Science","Advanced Intelligent Computing Technology and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-9849-3_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T14:51:59Z","timestamp":1765291919000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-9849-3_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819698486","9789819698493"],"references-count":38,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-9849-3_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"19 July 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ningbo","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 July 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 July 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/icg\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}