{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,21]],"date-time":"2025-12-21T17:05:10Z","timestamp":1766336710030,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":56,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,8,24]],"date-time":"2024-08-24T00:00:00Z","timestamp":1724457600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/https:\/\/doi.org\/10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFB4500300"],"award-info":[{"award-number":["2022YFB4500300"]}],"id":[{"id":"10.13039\/https:\/\/doi.org\/10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/https:\/\/doi.org\/10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62271452"],"award-info":[{"award-number":["62271452"]}],"id":[{"id":"10.13039\/https:\/\/doi.org\/10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,8,25]]},"DOI":"10.1145\/3637528.3671716","type":"proceedings-article","created":{"date-parts":[[2024,8,25]],"date-time":"2024-08-25T04:54:55Z","timestamp":1724561695000},"page":"3057-3068","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Unsupervised Heterogeneous Graph Rewriting Attack via Node Clustering"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5445-3676","authenticated-orcid":false,"given":"Haosen","family":"Wang","sequence":"first","affiliation":[{"name":"Southeast University &amp; Zhejiang Lab, Nanjing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7354-9374","authenticated-orcid":false,"given":"Can","family":"Xu","sequence":"additional","affiliation":[{"name":"East China Normal University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3332-0687","authenticated-orcid":false,"given":"Chenglong","family":"Shi","sequence":"additional","affiliation":[{"name":"Southeast University, Nanjing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-8153-8485","authenticated-orcid":false,"given":"Pengfei","family":"Zheng","sequence":"additional","affiliation":[{"name":"Zhejiang Lab, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-4425-8788","authenticated-orcid":false,"given":"Shiming","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3965-4215","authenticated-orcid":false,"given":"Minhao","family":"Cheng","sequence":"additional","affiliation":[{"name":"Pennsylvania State University, Philadelphia, PA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7626-0162","authenticated-orcid":false,"given":"Hongyang","family":"Chen","sequence":"additional","affiliation":[{"name":"Zhejiang Lab, Hangzhou, China"}]}],"member":"320","published-online":{"date-parts":[[2024,8,24]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"International Conference on Machine Learning. PMLR, 695--704","author":"Bojchevski Aleksandar","year":"2019","unstructured":"Aleksandar Bojchevski and Stephan G\u00fcnnemann. 2019. Adversarial attacks on node embeddings via graph poisoning. In International Conference on Machine Learning. PMLR, 695--704."},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623619"},{"key":"e_1_3_2_2_3_1","volume-title":"The Tenth International Conference on Learning Representations, ICLR 2022","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. In The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25--29, 2022. https:\/\/openreview. net\/forum?id=wkMG8cdvh7-"},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330673"},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380297"},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583454"},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3357875"},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330970"},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380027"},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599503"},{"key":"e_1_3_2_2_11_1","volume-title":"5th International Conference on Learning Representations, ICLR","author":"Jang Eric","year":"2017","unstructured":"Eric Jang, Shixiang Gu, and Ben Poole. 2017. Categorical Reparameterization with Gumbel-Softmax. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24--26, 2017, Conference Track Proceedings. OpenReview.net. https:\/\/openreview.net\/forum?id=rkE3y85ee"},{"key":"e_1_3_2_2_12_1","volume-title":"Adversarial attacks and defenses on graphs: A review and empirical study. arXiv preprint arXiv:2003.00653 10, 3447556.3447566","author":"Jin Wei","year":"2020","unstructured":"Wei Jin, Yaxin Li, Han Xu, Yiqi Wang, and Jiliang Tang. 2020. Adversarial attacks and defenses on graphs: A review and empirical study. arXiv preprint arXiv:2003.00653 10, 3447556.3447566 (2020)."},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449971"},{"key":"e_1_3_2_2_14_1","volume-title":"Adam: A method for stochastic optimization. ICLR.","author":"Kingma Diederik P","year":"2014","unstructured":"Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. ICLR."},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDMW58026.2022.00096"},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2023.07.009"},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE51399.2021.00084"},{"key":"e_1_3_2_2_18_1","volume-title":"HetDDI: a pre-trained heterogeneous graph neural network model for drug--drug interaction prediction. Briefings in Bioinformatics 24, 6","author":"Li Zhe","year":"2023","unstructured":"Zhe Li, Xinyi Tu, Yuping Chen, and Wenbin Lin. 2023. HetDDI: a pre-trained heterogeneous graph neural network model for drug--drug interaction prediction. Briefings in Bioinformatics 24, 6 (2023), bbad385."},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467142"},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401253"},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467416"},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583493"},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5985"},{"key":"e_1_3_2_2_24_1","volume-title":"An end-to-end heterogeneous graph representation learning-based framework for drug--target interaction prediction. Briefings in bioinformatics 22, 5","author":"Peng Jiajie","year":"2021","unstructured":"Jiajie Peng, Yuxian Wang, Jiaojiao Guan, Jingyi Li, Ruijiang Han, Jianye Hao, Zhongyu Wei, and Xuequn Shang. 2021. An end-to-end heterogeneous graph representation learning-based framework for drug--target interaction prediction. Briefings in bioinformatics 22, 5 (2021)."},{"key":"e_1_3_2_2_25_1","first-page":"26911","article-title":"Distilling meta knowledge on heterogeneous graph for illicit drug trafficker detection on social media","volume":"34","author":"Qian Yiyue","year":"2021","unstructured":"Yiyue Qian, Yiming Zhang, Yanfang Ye, Chuxu Zhang, et al. 2021. Distilling meta knowledge on heterogeneous graph for illicit drug trafficker detection on social media. Advances in Neural Information Processing Systems 34 (2021), 26911--26923.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_26_1","volume-title":"Heterogeneous deep graph infomax. arXiv preprint arXiv:1911.08538","author":"Ren Yuxiang","year":"2019","unstructured":"Yuxiang Ren, Bo Liu, Chao Huang, Peng Dai, Liefeng Bo, and Jiawei Zhang. 2019. Heterogeneous deep graph infomax. arXiv preprint arXiv:1911.08538 (2019)."},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1007\/978--3--319-"},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3583780.3615095"},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3273255"},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2022.3201243"},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482393"},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i8.26192"},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482277"},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313562"},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467415"},{"key":"e_1_3_2_2_36_1","volume-title":"Proceedings of the 2023 SIAM International Conference on Data Mining (SDM). SIAM, 136--144","author":"Li Qi","year":"2023","unstructured":"ZehongWang, Qi Li, Donghua Yu, Xiaolong Han, Xiao-Zhi Gao, and Shigen Shen. 2023. Heterogeneous graph contrastive multi-view learning. In Proceedings of the 2023 SIAM International Conference on Data Mining (SDM). SIAM, 136--144."},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/550"},{"key":"e_1_3_2_2_38_1","volume-title":"Metapath-guided dual semantic-aware filtering for HIN-based recommendation. The Journal of Supercomputing","author":"Yan Surong","year":"2023","unstructured":"Surong Yan, HaosenWang, Yixiao Li, ChunqiWu, Long Han, Chenglong Shi, and Ruilin Guo. 2023. Metapath-guided dual semantic-aware filtering for HIN-based recommendation. The Journal of Supercomputing (2023), 1--31."},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.114601"},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i9.26283"},{"key":"e_1_3_2_2_41_1","volume-title":"Graph Contrastive Learning with Augmentations. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020","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. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6--12, 2020, virtual. https:\/\/proceedings.neurips.cc\/paper\/2020\/hash\/ 3fe230348e9a12c13120749e3f9fa4cd-Abstract.html"},{"key":"e_1_3_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611977653.ch5"},{"key":"e_1_3_2_2_43_1","volume-title":"Mitigating Severe Robustness Degradation on Graphs. In The Twelfth International Conference on Learning Representations.","author":"Yuan Xiangchi","year":"2023","unstructured":"Xiangchi Yuan, Chunhui Zhang, Yijun Tian, Yanfang Ye, and Chuxu Zhang. 2023. Mitigating Severe Robustness Degradation on Graphs. In The Twelfth International Conference on Learning Representations."},{"key":"e_1_3_2_2_44_1","volume-title":"Black-box Gradient Attack on Graph Neural Networks: Deeper Insights in Graph-based Attack and Defense. abs\/2104.15061","author":"Zhan Haoxi","year":"2021","unstructured":"Haoxi Zhan and Xiaobing Pei. 2021. Black-box Gradient Attack on Graph Neural Networks: Deeper Insights in Graph-based Attack and Defense. abs\/2104.15061 (2021). https:\/\/arxiv.org\/abs\/2104.15061"},{"key":"e_1_3_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330961"},{"key":"e_1_3_2_2_46_1","volume-title":"The Eleventh International Conference on Learning Representations, ICLR 2023","author":"Zhang Chunhui","year":"2023","unstructured":"Chunhui Zhang, Yijun Tian, Mingxuan Ju, Zheyuan Liu, Yanfang Ye, Nitesh V. Chawla, and Chuxu Zhang. 2023. Chasing All-Round Graph Representation Robustness: Model, Training, and Optimization. In The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1--5, 2023."},{"key":"e_1_3_2_2_47_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i4.20357"},{"key":"e_1_3_2_2_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512179"},{"key":"e_1_3_2_2_49_1","doi-asserted-by":"crossref","unstructured":"Jianan Zhao Xiao Wang Chuan Shi Zekuan Liu and Yanfang Ye. 2020. Network schema preserving heterogeneous information network embedding. In International joint conference on artificial intelligence (IJCAI).","DOI":"10.24963\/ijcai.2020\/190"},{"key":"e_1_3_2_2_50_1","volume-title":"Adversarial Robustness in Graph Neural Networks: A Hamiltonian Approach. In Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023","author":"Zhao Kai","year":"2023","unstructured":"Kai Zhao, Qiyu Kang, Yang Song, Rui She, Sijie Wang, and Wee Peng Tay. 2023. Adversarial Robustness in Graph Neural Networks: A Hamiltonian Approach. In Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023."},{"key":"e_1_3_2_2_51_1","volume-title":"Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, NeurIPS Datasets and Benchmarks 2021","author":"Zheng Qinkai","year":"2021","unstructured":"Qinkai Zheng, Xu Zou, Yuxiao Dong, Yukuo Cen, Da Yin, Jiarong Xu, Yang Yang, and Jie Tang. 2021. Graph Robustness Benchmark: Benchmarking the Adversarial Robustness of Graph Machine Learning. In Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, NeurIPS Datasets and Benchmarks 2021, December 2021, virtual."},{"volume-title":"d.]. Simple and Efficient Partial Graph Adversarial Attack: A New Perspective","author":"Zhu Guanghui","key":"e_1_3_2_2_52_1","unstructured":"Guanghui Zhu, Mengyu Chen, Chunfeng Yuan, and Yihua Huang. [n. d.]. Simple and Efficient Partial Graph Adversarial Attack: A New Perspective. IEEE Transactions on Knowledge and Data Engineering ([n. d.])."},{"key":"e_1_3_2_2_53_1","volume-title":"Structure-aware hard negative mining for heterogeneous graph contrastive learning. arXiv preprint arXiv:2108.13886","author":"Zhu Yanqiao","year":"2021","unstructured":"Yanqiao Zhu, Yichen Xu, Hejie Cui, Carl Yang, Qiang Liu, and Shu Wu. 2021. Structure-aware hard negative mining for heterogeneous graph contrastive learning. arXiv preprint arXiv:2108.13886 (2021)."},{"key":"e_1_3_2_2_54_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449802"},{"key":"e_1_3_2_2_55_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220078"},{"key":"e_1_3_2_2_56_1","volume-title":"7th International Conference on Learning Representations, ICLR 2019","author":"Z\u00fcgner Daniel","year":"2019","unstructured":"Daniel Z\u00fcgner and Stephan G\u00fcnnemann. 2019. Adversarial Attacks on Graph Neural Networks via Meta Learning. In 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6--9, 2019."}],"event":{"name":"KDD '24: The 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"],"location":"Barcelona Spain","acronym":"KDD '24"},"container-title":["Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3637528.3671716","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3637528.3671716","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:06:00Z","timestamp":1750291560000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3637528.3671716"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,24]]},"references-count":56,"alternative-id":["10.1145\/3637528.3671716","10.1145\/3637528"],"URL":"https:\/\/doi.org\/10.1145\/3637528.3671716","relation":{},"subject":[],"published":{"date-parts":[[2024,8,24]]},"assertion":[{"value":"2024-08-24","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}