{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T18:36:50Z","timestamp":1772908610714,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":55,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,8,14]],"date-time":"2022-08-14T00:00:00Z","timestamp":1660435200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-sa\/4.0\/"}],"funder":[{"name":"Youth Innovation Promotion Association CAS, Beijing Nova Program","award":["Z201100006820062"],"award-info":[{"award-number":["Z201100006820062"]}]},{"name":"Alibaba Innovative Research Program"},{"name":"China Scholarship Council"},{"name":"National Natural Science Foundation of China under Grant","award":["No.61976204, U1811461, U1836206"],"award-info":[{"award-number":["No.61976204, U1811461, U1836206"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,8,14]]},"DOI":"10.1145\/3534678.3539484","type":"proceedings-article","created":{"date-parts":[[2022,8,12]],"date-time":"2022-08-12T19:06:41Z","timestamp":1660331201000},"page":"925-935","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":60,"title":["Reliable Representations Make A Stronger Defender"],"prefix":"10.1145","author":[{"given":"Kuan","family":"Li","sequence":"first","affiliation":[{"name":"Institute of Computing Technology, Chinese Academy of Sciences & University of Chinese Academy of Sciences, Beijing, China"}]},{"given":"Yang","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute of Computing Technology, Chinese Academy of Sciences & University of Chinese Academy of Sciences, Beijing, China"}]},{"given":"Xiang","family":"Ao","sequence":"additional","affiliation":[{"name":"Institute of Computing Technology, Chinese Academy of Sciences & University of Chinese Academy of Sciences, Beijing, China"}]},{"given":"Jianfeng","family":"Chi","sequence":"additional","affiliation":[{"name":"Alibaba Group, Hangzhou, China"}]},{"given":"Jinghua","family":"Feng","sequence":"additional","affiliation":[{"name":"Alibaba Group, Hangzhou, China"}]},{"given":"Hao","family":"Yang","sequence":"additional","affiliation":[{"name":"Alibaba Group, Hangzhou, China"}]},{"given":"Qing","family":"He","sequence":"additional","affiliation":[{"name":"Institute of Computing Technology, Chinese Academy of Sciences & University of Chinese Academy of Sciences, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2022,8,14]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Greg Ver Steeg, and Aram Galstyan","author":"Abu-El-Haija Sami","year":"2019","unstructured":"Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Nazanin Alipourfard, Kristina Lerman, Hrayr Harutyunyan, Greg Ver Steeg, and Aram Galstyan. 2019. Mixhop: Higher-order Graph Convolutional Architectures via Sparsified Neighborhood Mixing. In ICML. PMLR, 21--29."},{"key":"e_1_3_2_2_2_1","unstructured":"Peter W Battaglia Jessica B Hamrick Victor Bapst Alvaro Sanchez-Gonzalez Vinicius Zambaldi Mateusz Malinowski Andrea Tacchetti David Raposo Adam Santoro Ryan Faulkner et al. 2018. Relational Inductive Biases Deep Learning and Graph Networks. arXiv preprint arXiv:1806.01261 (2018)."},{"key":"e_1_3_2_2_3_1","volume-title":"Understanding Structural Vulnerability in Graph Convolutional Networks. arXiv preprint arXiv:2108.06280","author":"Chen Liang","year":"2021","unstructured":"Liang Chen, Jintang Li, Qibiao Peng, Yang Liu, Zibin Zheng, and Carl Yang. 2021. Understanding Structural Vulnerability in Graph Convolutional Networks. arXiv preprint arXiv:2108.06280 (2021)."},{"key":"e_1_3_2_2_4_1","unstructured":"Ting Chen Simon Kornblith Mohammad Norouzi and Geoffrey Hinton. 2020 a. A Simple Framework for Contrastive Learning of Visual Representations. In ICML."},{"key":"e_1_3_2_2_5_1","volume-title":"Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings. NeurIPS","volume":"33","author":"Chen Yu","year":"2020","unstructured":"Yu Chen, Lingfei Wu, and Mohammed Zaki. 2020 b. Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings. NeurIPS, Vol. 33."},{"key":"e_1_3_2_2_6_1","unstructured":"Eli Chien Jianhao Peng Pan Li and Olgica Milenkovic. 2021. Adaptive Universal Generalized Pagerank Graph Neural Network. In ICLR."},{"key":"e_1_3_2_2_7_1","unstructured":"Hanjun Dai Hui Li Tian Tian Xin Huang Lin Wang Jun Zhu and Le Song. 2018. Adversarial Attack on Graph Structured Data. In ICML. 1115--1124."},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"crossref","unstructured":"Negin Entezari Saba A Al-Sayouri Amirali Darvishzadeh and Evangelos E Papalexakis. 2020. All You Need is Low (rank) Defending Against Adversarial Attacks on Graphs. In WSDM. 169--177.","DOI":"10.1145\/3336191.3371789"},{"key":"e_1_3_2_2_9_1","volume-title":"Layla El Asri, and Seyed Mehran Kazemi","author":"Fatemi Bahare","year":"2021","unstructured":"Bahare Fatemi, Layla El Asri, and Seyed Mehran Kazemi. 2021. SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks."},{"key":"e_1_3_2_2_10_1","volume-title":"Single-Node Attack for Fooling Graph Neural Networks. arXiv preprint arXiv:2011.03574","author":"Finkelshtein Ben","year":"2020","unstructured":"Ben Finkelshtein, Chaim Baskin, Evgenii Zheltonozhskii, and Uri Alon. 2020. Single-Node Attack for Fooling Graph Neural Networks. arXiv preprint arXiv:2011.03574 (2020)."},{"key":"e_1_3_2_2_11_1","volume-title":"Exploring Structure-adaptive Graph Learning for Robust Semi-supervised Classification","author":"Gao Xiang","unstructured":"Xiang Gao, Wei Hu, and Zongming Guo. 2020. Exploring Structure-adaptive Graph Learning for Robust Semi-supervised Classification. In ICME. IEEE."},{"key":"e_1_3_2_2_12_1","volume-title":"NeurIPS","volume":"30","author":"Duran Alberto Garcia","year":"2017","unstructured":"Alberto Garcia Duran and Mathias Niepert. 2017. Learning Graph Representations With Embedding Propagation. In NeurIPS, Vol. 30."},{"key":"e_1_3_2_2_13_1","unstructured":"Simon Geisler Daniel Z\u00fcgner and Stephan G\u00fcnnemann. 2020. Reliable Graph Neural Networks via Robust Aggregation. In NeurIPS."},{"key":"e_1_3_2_2_14_1","unstructured":"William L Hamilton Rex Ying and Jure Leskovec. 2017. Inductive Representation Learning on Large Graphs. In NeruIPS. 1025--1035."},{"key":"e_1_3_2_2_15_1","unstructured":"Kaveh Hassani and Amir Hosein Khasahmadi. 2020. Contrastive Multi-view Representation Learning on Graphs. In ICML. PMLR 4116--4126."},{"key":"e_1_3_2_2_16_1","unstructured":"R Devon Hjelm Alex Fedorov Samuel Lavoie-Marchildon Karan Grewal Phil Bachman Adam Trischler and Yoshua Bengio. 2019. Learning Deep Representations by Mutual Information Estimation and Maximization. In ICLR."},{"key":"e_1_3_2_2_17_1","volume-title":"Hongzhi Chen, and Ming-Chang Yang.","author":"Hou Yifan","year":"2019","unstructured":"Yifan Hou, Jian Zhang, James Cheng, Kaili Ma, Richard TB Ma, Hongzhi Chen, and Ming-Chang Yang. 2019. Measuring and Improving The Use of Graph Information in Graph Neural Networks. In ICLR."},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"crossref","unstructured":"Mengda Huang Yang Liu Xiang Ao Kuan Li Jianfeng Chi Jinghua Feng Hao Yang and Qing He. 2022. AUC-oriented Graph Neural Network for Fraud Detection. In WWW.","DOI":"10.1145\/3485447.3512178"},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"crossref","unstructured":"Glen Jeh and Jennifer Widom. 2003. Scaling Personalized Web Search. In WWW.","DOI":"10.1145\/775189.775191"},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"crossref","unstructured":"Wei Jin Tyler Derr Yiqi Wang Yao Ma Zitao Liu and Jiliang Tang. 2021. Node Similarity Preserving Graph Convolutional Networks. In WSDM. 148--156.","DOI":"10.1145\/3437963.3441735"},{"key":"e_1_3_2_2_21_1","unstructured":"Wei Jin Yaxin Li Han Xu Yiqi Wang Shuiwang Ji Charu Aggarwal and Jiliang Tang. 2020 a. Adversarial Attacks and Defenses on Graphs: A Review A Tool and Empirical Studies. In KDD Explorations."},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"crossref","unstructured":"Wei Jin Yao Ma Xiaorui Liu Xianfeng Tang Suhang Wang and Jiliang Tang. 2020 b. Graph Structure Learning for Robust Graph Neural Networks. In KDD.","DOI":"10.1145\/3394486.3403049"},{"key":"e_1_3_2_2_23_1","volume-title":"Variational Graph Auto-Encoders. NIPS Workshop on Bayesian Deep Learning","author":"Kipf Thomas N","year":"2016","unstructured":"Thomas N Kipf and Max Welling. 2016. Variational Graph Auto-Encoders. NIPS Workshop on Bayesian Deep Learning (2016)."},{"key":"e_1_3_2_2_24_1","volume-title":"Kipf and Max Welling","author":"Thomas","year":"2017","unstructured":"Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In ICLR."},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11691"},{"key":"e_1_3_2_2_26_1","volume-title":"Deeprobust: A Pytorch Library for Adversarial Attacks and Defenses. arXiv preprint arXiv:2005.06149","author":"Li Yaxin","year":"2020","unstructured":"Yaxin Li, Wei Jin, Han Xu, and Jiliang Tang. 2020. Deeprobust: A Pytorch Library for Adversarial Attacks and Defenses. arXiv preprint arXiv:2005.06149 (2020)."},{"key":"e_1_3_2_2_27_1","unstructured":"Yujia Li Daniel Tarlow Marc Brockschmidt and Richard Zemel. 2016. Gated Graph Sequence Neural Networks. ICLR."},{"key":"e_1_3_2_2_28_1","unstructured":"Xiaorui Liu Wei Jin Yao Ma Yaxin Li Hua Liu Yiqi Wang Ming Yan and Jiliang Tang. 2021 b. Elastic Graph Neural Networks. In ICML. PMLR 6837--6849."},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"crossref","unstructured":"Yang Liu Xiang Ao Zidi Qin Jianfeng Chi Jinghua Feng Hao Yang and Qing He. 2021 a. Pick and Choose: A GNN-based Imbalanced Learning Approach for Fraud Detection. In WWW. 3168--3177.","DOI":"10.1145\/3442381.3449989"},{"key":"e_1_3_2_2_30_1","unstructured":"Dongsheng Luo Wei Cheng Wenchao Yu Bo Zong Jingchao Ni Haifeng Chen and Xiang Zhang. 2021. Learning to Drop: Robust Graph Neural Network via Topological Denoising. In WSDM. 779--787."},{"key":"e_1_3_2_2_31_1","volume-title":"Birds of a Feather: Homophily in Social Networks. Annual review of sociology","author":"McPherson Miller","year":"2001","unstructured":"Miller McPherson, Lynn Smith-Lovin, and James M Cook. 2001. Birds of a Feather: Homophily in Social Networks. Annual review of sociology, Vol. 27, 1 (2001), 415--444."},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"crossref","unstructured":"Zhen Peng Wenbing Huang Minnan Luo Qinghua Zheng Yu Rong Tingyang Xu and Junzhou Huang. 2020. Graph Representation Learning via Graphical Mutual Information Maximization. In WWW.","DOI":"10.1145\/3366423.3380112"},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623732"},{"key":"e_1_3_2_2_34_1","volume-title":"Infograph: Unsupervised and Semi-supervised Graph-level Representation Learning via Mutual Information maximization. In ICLR.","author":"Sun Fan-Yun","year":"2020","unstructured":"Fan-Yun Sun, Jordan Hoffmann, Vikas Verma, and Jian Tang. 2020. Infograph: Unsupervised and Semi-supervised Graph-level Representation Learning via Mutual Information maximization. In ICLR."},{"key":"e_1_3_2_2_35_1","unstructured":"Petar Velivc kovi\u0107 Guillem Cucurull Arantxa Casanova Adriana Romero Pietro Lio and Yoshua Bengio. 2017. Graph Attention Networks. In ICLR."},{"key":"e_1_3_2_2_36_1","volume-title":"Deep Graph Infomax. arXiv preprint arXiv:1809.10341","author":"Petar Velivc","year":"2018","unstructured":"Petar Velivc kovi\u0107, 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_37_1","volume-title":"Am-gcn: Adaptive Multi-channel Graph Convolutional Networks. In KDD. 1243--1253.","author":"Wang Xiao","year":"2020","unstructured":"Xiao Wang, Meiqi Zhu, Deyu Bo, Peng Cui, Chuan Shi, and Jian Pei. 2020. Am-gcn: Adaptive Multi-channel Graph Convolutional Networks. In KDD. 1243--1253."},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41562-017-0290-3"},{"key":"e_1_3_2_2_39_1","unstructured":"Olivia Wiles Sven Gowal Florian Stimberg Sylvestre Alvise-Rebuffi Ira Ktena Taylan Cemgil et al. 2022. A Fine-grained Analysis on Distribution Shift. In ICLR."},{"key":"e_1_3_2_2_40_1","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. IJCAI."},{"key":"e_1_3_2_2_41_1","unstructured":"Kaidi Xu Hongge Chen Sijia Liu Pin-Yu Chen Tsui-Wei Weng Mingyi Hong and Xue Lin. 2019 a. Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective. IJCAI."},{"key":"e_1_3_2_2_42_1","unstructured":"Keyulu Xu Weihua Hu Jure Leskovec and Stefanie Jegelka. 2019 b. How Powerful Are Graph Neural Networks?. In ICLR."},{"key":"e_1_3_2_2_43_1","unstructured":"Yuning You Tianlong Chen Yongduo Sui Ting Chen Zhangyang Wang and Yang Shen. 2020. Graph Contrastive Learning With Augmentations. NeurIPS."},{"key":"e_1_3_2_2_44_1","volume-title":"Graph-revised Convolutional Network","author":"Yu Donghan","unstructured":"Donghan Yu, Ruohong Zhang, Zhengbao Jiang, Yuexin Wu, and Yiming Yang. 2020. Graph-revised Convolutional Network. In ECML. Springer, 378--393."},{"key":"e_1_3_2_2_45_1","unstructured":"Xiang Zhang and Marinka Zitnik. 2020. GNNGuard: Defending Graph Neural Networks Against Adversarial Attacks. NeurIPS."},{"key":"e_1_3_2_2_46_1","volume-title":"Deep learning on graphs: A survey","author":"Zhang Ziwei","year":"2020","unstructured":"Ziwei Zhang, Peng Cui, and Wenwu Zhu. 2020. Deep learning on graphs: A survey. IEEE Transactions on Knowledge and Data Engineering (2020)."},{"key":"e_1_3_2_2_47_1","doi-asserted-by":"crossref","unstructured":"Tong Zhao Yozen Liu Leonardo Neves Oliver Woodford Meng Jiang and Neil Shah. 2021. Data Augmentation for Graph Neural Networks. In AAAI.","DOI":"10.1609\/aaai.v35i12.17315"},{"key":"e_1_3_2_2_48_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.aiopen.2021.01.001"},{"key":"e_1_3_2_2_49_1","unstructured":"Dingyuan Zhu Ziwei Zhang Peng Cui and Wenwu Zhu. 2019. Robust Graph Convolutional Networks Against Adversarial Attacks. In KDD. 1399--1407."},{"key":"e_1_3_2_2_50_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.xinn.2021.100176"},{"key":"e_1_3_2_2_51_1","doi-asserted-by":"crossref","unstructured":"Yulin Zhu Yuni Lai Kaifa Zhao Xiapu Luo Mingquan Yuan Jian Ren and Kai Zhou. 2022. BinarizedAttack: Structural Poisoning Attacks to Graph-based Anomaly Detection. In ICDE.","DOI":"10.1109\/ICDE53745.2022.00006"},{"key":"e_1_3_2_2_52_1","unstructured":"Yanqiao Zhu Weizhi Xu Jinghao Zhang Qiang Liu Shu Wu and Liang Wang. 2021 b. Deep Graph Structure Learning for Robust Representations: A Survey. arXiv preprint arXiv:2103.03036."},{"key":"e_1_3_2_2_53_1","volume-title":"Deep Graph Contrastive Representation Learning. In ICML Workshop on Graph Representation Learning and Beyond. http:\/\/arxiv.org\/abs\/2006","author":"Zhu Yanqiao","year":"2020","unstructured":"Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, and Liang Wang. 2020. Deep Graph Contrastive Representation Learning. In ICML Workshop on Graph Representation Learning and Beyond. http:\/\/arxiv.org\/abs\/2006.04131"},{"key":"e_1_3_2_2_54_1","doi-asserted-by":"crossref","unstructured":"Daniel Z\u00fcgner Amir Akbarnejad and Stephan G\u00fcnnemann. 2018. Adversarial Attacks on Neural Networks for Graph Data. In KDD. 2847--2856.","DOI":"10.1145\/3219819.3220078"},{"key":"e_1_3_2_2_55_1","doi-asserted-by":"crossref","unstructured":"Daniel Z\u00fcgner and Stephan G\u00fcnnemann. 2019. Adversarial Attacks on Graph Neural Networks via Meta Learning. In ICLR.","DOI":"10.24963\/ijcai.2019\/872"}],"event":{"name":"KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Washington DC USA","acronym":"KDD '22","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3534678.3539484","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3534678.3539484","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:03:04Z","timestamp":1750186984000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3534678.3539484"}},"subtitle":["Unsupervised Structure Refinement for Robust GNN"],"short-title":[],"issued":{"date-parts":[[2022,8,14]]},"references-count":55,"alternative-id":["10.1145\/3534678.3539484","10.1145\/3534678"],"URL":"https:\/\/doi.org\/10.1145\/3534678.3539484","relation":{},"subject":[],"published":{"date-parts":[[2022,8,14]]},"assertion":[{"value":"2022-08-14","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}