{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T10:03:38Z","timestamp":1775815418733,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":48,"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:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Research Grants Council of the Hong Kong Special Administrative Region","award":["CUHK 2410021, Research Impact Fund, No. R5034-18"],"award-info":[{"award-number":["CUHK 2410021, Research Impact Fund, No. R5034-18"]}]},{"name":"National Key Research and Development Program of China","award":["No. 2018AAA0100204"],"award-info":[{"award-number":["No. 2018AAA0100204"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,8,14]]},"DOI":"10.1145\/3534678.3539425","type":"proceedings-article","created":{"date-parts":[[2022,8,12]],"date-time":"2022-08-12T19:06:12Z","timestamp":1660331172000},"page":"2524-2534","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":92,"title":["COSTA"],"prefix":"10.1145","author":[{"given":"Yifei","family":"Zhang","sequence":"first","affiliation":[{"name":"The Chinese University of Hong Kong, Hong Kong SAR, China"}]},{"given":"Hao","family":"Zhu","sequence":"additional","affiliation":[{"name":"Australian National University and Data61\/CSIRO, Canberra, Australia"}]},{"given":"Zixing","family":"Song","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong, Hong Kong SAR, China"}]},{"given":"Piotr","family":"Koniusz","sequence":"additional","affiliation":[{"name":"Data61\/CSIRO & Australian National University, Canberra, Australia"}]},{"given":"Irwin","family":"King","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong, Hong Kong SAR, China"}]}],"member":"320","published-online":{"date-parts":[[2022,8,14]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Learning representations by maximizing mutual information across views. arXiv preprint arXiv:1906.00910","author":"Bachman Philip","year":"2019","unstructured":"Philip Bachman, R Devon Hjelm, and William Buchwalter. 2019. Learning representations by maximizing mutual information across views. arXiv preprint arXiv:1906.00910 (2019)."},{"key":"e_1_3_2_2_2_1","volume-title":"Graph Barlow Twins: A self-supervised representation learning framework for graphs. arXiv preprint arXiv:2106.02466","author":"Bielak Piotr","year":"2021","unstructured":"Piotr Bielak, Tomasz Kajdanowicz, and Nitesh V Chawla. 2021. Graph Barlow Twins: A self-supervised representation learning framework for graphs. arXiv preprint arXiv:2106.02466 (2021)."},{"key":"e_1_3_2_2_3_1","volume-title":"International conference on machine learning. PMLR, 1597--1607","author":"Chen Ting","year":"2020","unstructured":"Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020. A simple framework for contrastive learning of visual representations. In International conference on machine learning. PMLR, 1597--1607."},{"key":"e_1_3_2_2_4_1","volume-title":"Modeling Scale-free Graphs with Hyperbolic Geometry for Knowledge-aware Recommendation. In WSDM '22: The Fifteenth ACM International Conference on Web Search and Data Mining. ACM.","author":"Chen Yankai","year":"2022","unstructured":"Yankai Chen, Menglin Yang, Yingxue Zhang, Mengchen Zhao, Ziqiao Meng, Jianye Hao, and Irwin King. 2022. Modeling Scale-free Graphs with Hyperbolic Geometry for Knowledge-aware Recommendation. In WSDM '22: The Fifteenth ACM International Conference on Web Search and Data Mining. ACM."},{"key":"e_1_3_2_2_5_1","volume-title":"Attentive Knowledge-aware Graph Convolutional Networks with Collaborative Guidance for Personalized Recommendation. In The 38th IEEE International Conference on Data Engineering.","author":"Chen Yankai","year":"2022","unstructured":"Yankai Chen, Yaming Yang, Yujing Wang, Jing Bai, Xiangchen Song, and Irwin King. 2022. Attentive Knowledge-aware Graph Convolutional Networks with Collaborative Guidance for Personalized Recommendation. In The 38th IEEE International Conference on Data Engineering."},{"key":"e_1_3_2_2_6_1","volume-title":"Dataset augmentation in feature space. arXiv preprint arXiv:1702.05538","author":"DeVries Terrance","year":"2017","unstructured":"Terrance DeVries and Graham W Taylor. 2017. Dataset augmentation in feature space. arXiv preprint arXiv:1702.05538 (2017)."},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1137\/S0097539704442684"},{"key":"e_1_3_2_2_8_1","volume-title":"A survey of data augmentation approaches for nlp. arXiv preprint arXiv:2105.03075","author":"Feng Steven Y","year":"2021","unstructured":"Steven Y Feng, Varun Gangal, Jason Wei, Sarath Chandar, Soroush Vosoughi, Teruko Mitamura, and Eduard Hovy. 2021. A survey of data augmentation approaches for nlp. arXiv preprint arXiv:2105.03075 (2021)."},{"key":"e_1_3_2_2_9_1","volume-title":"SimCSE: Simple Contrastive Learning of Sentence Embeddings. arXiv preprint arXiv:2104.08821","author":"Gao Tianyu","year":"2021","unstructured":"Tianyu Gao, Xingcheng Yao, and Danqi Chen. 2021. SimCSE: Simple Contrastive Learning of Sentence Embeddings. arXiv preprint arXiv:2104.08821 (2021)."},{"key":"e_1_3_2_2_10_1","volume-title":"A generalization of the Eckart-Young-Mirsky matrix approximation theorem. Linear Algebra and its applications 88","author":"Golub Gene H","year":"1987","unstructured":"Gene H Golub, Alan Hoffman, and Gilbert W Stewart. 1987. A generalization of the Eckart-Young-Mirsky matrix approximation theorem. Linear Algebra and its applications 88 (1987), 317--327."},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939754"},{"key":"e_1_3_2_2_12_1","volume-title":"Graphcl: Contrastive self-supervised learning of graph representations. arXiv preprint arXiv:2007.08025","author":"Hafidi Hakim","year":"2020","unstructured":"Hakim Hafidi, Mounir Ghogho, Philippe Ciblat, and Ananthram Swami. 2020. Graphcl: Contrastive self-supervised learning of graph representations. arXiv preprint arXiv:2007.08025 (2020)."},{"key":"e_1_3_2_2_13_1","volume-title":"Proceedings of the 31st International Conference on Neural Information Processing Systems. 1025--1035","author":"Hamilton William L","year":"2017","unstructured":"William L Hamilton, Rex Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. In Proceedings of the 31st International Conference on Neural Information Processing Systems. 1025--1035."},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.328"},{"key":"e_1_3_2_2_15_1","volume-title":"International Conference on Machine Learning. PMLR, 4116--4126","author":"Hassani Kaveh","year":"2020","unstructured":"Kaveh Hassani and Amir Hosein Khasahmadi. 2020. Contrastive multi-view representation learning on graphs. In International Conference on Machine Learning. PMLR, 4116--4126."},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"e_1_3_2_2_17_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_18_1","volume-title":"Variational graph auto-encoders. arXiv preprint arXiv:1611.07308","author":"Kipf Thomas N","year":"2016","unstructured":"Thomas N Kipf and Max Welling. 2016. Variational graph auto-encoders. arXiv preprint arXiv:1611.07308 (2016)."},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3107164"},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/1150402.1150436"},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/2487575.2487623"},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/2766462.2767755"},{"key":"e_1_3_2_2_23_1","volume-title":"Wiki-cs: A wikipedia-based benchmark for graph neural networks. arXiv preprint arXiv:2007.02901","author":"Mernyei P\u00e9ter","year":"2020","unstructured":"P\u00e9ter Mernyei and C\u0103t\u0103lina Cangea. 2020. Wiki-cs: A wikipedia-based benchmark for graph neural networks. arXiv preprint arXiv:2007.02901 (2020)."},{"key":"e_1_3_2_2_25_1","volume-title":"Scikit-learn: Machine learning in Python. the Journal of machine Learning research 12","author":"Pedregosa Fabian","year":"2011","unstructured":"Fabian Pedregosa, Ga\u00ebl Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al. 2011. Scikit-learn: Machine learning in Python. the Journal of machine Learning research 12 (2011), 2825--2830."},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380112"},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623732"},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-019-0197-0"},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/2740908.2742839"},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"crossref","unstructured":"Zixing Song Ziqiao Meng Yifei Zhang and Irwin King. 2021. Semi-supervised Multi-label Learning for Graph-structured Data. In CIKM. ACM 1723--1733.","DOI":"10.1145\/3459637.3482391"},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS"},{"key":"e_1_3_2_2_32_1","volume-title":"Fisher-Bures Adversary Graph Convolutional Networks. Conference on Uncertainty in Artificial Intelligence 115","author":"Sun Ke","year":"2019","unstructured":"Ke Sun, Piotr Koniusz, and Zhen Wang. 2019. Fisher-Bures Adversary Graph Convolutional Networks. Conference on Uncertainty in Artificial Intelligence 115 (2019), 465--475."},{"key":"e_1_3_2_2_33_1","volume-title":"Adversarial Graph Augmentation to Improve Graph Contrastive Learning. CoRR abs\/2106.05819","author":"Suresh Susheel","year":"2021","unstructured":"Susheel Suresh, Pan Li, Cong Hao, and Jennifer Neville. 2021. Adversarial Graph Augmentation to Improve Graph Contrastive Learning. CoRR abs\/2106.05819 (2021)."},{"key":"e_1_3_2_2_34_1","volume-title":"Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020","author":"Tian Yonglong","year":"2020","unstructured":"Yonglong Tian, Chen Sun, Ben Poole, Dilip Krishnan, Cordelia Schmid, and Phillip Isola. 2020. What Makes for Good Views for Contrastive Learning?. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020."},{"key":"e_1_3_2_2_35_1","volume-title":"Deep Graph Infomax. ICLR (Poster)","author":"Velickovic Petar","year":"2019","unstructured":"Petar Velickovic, William Fedus, William L Hamilton, Pietro Li\u00f2, Yoshua Bengio, and R Devon Hjelm. 2019. Deep Graph Infomax. ICLR (Poster) (2019)."},{"key":"e_1_3_2_2_36_1","first-page":"12635","article-title":"Implicit semantic data augmentation for deep networks","volume":"32","author":"Wang Yulin","year":"2019","unstructured":"Yulin Wang, Xuran Pan, Shiji Song, Hong Zhang, Gao Huang, and Cheng Wu. 2019. Implicit semantic data augmentation for deep networks. Advances in Neural Information Processing Systems 32 (2019), 12635--12644.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_37_1","volume-title":"Eda: Easy data augmentation techniques for boosting performance on text classification tasks. arXiv preprint arXiv:1901.11196","author":"Wei Jason","year":"2019","unstructured":"Jason Wei and Kai Zou. 2019. Eda: Easy data augmentation techniques for boosting performance on text classification tasks. arXiv preprint arXiv:1901.11196 (2019)."},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462862"},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM50108.2020.00082"},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512118"},{"key":"e_1_3_2_2_41_1","first-page":"5812","article-title":"Graph contrastive learning with augmentations","volume":"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.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_42_1","volume-title":"Are Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendation. arXiv preprint arXiv:2112.08679","author":"Yu Junliang","year":"2022","unstructured":"Junliang Yu, Hongzhi Yin, Xin Xia, Tong Chen, Lizhen Cui, and Nguyen Quoc Viet Hung. 2022. Are Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendation. arXiv preprint arXiv:2112.08679 (2022)."},{"key":"e_1_3_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1"},{"key":"e_1_3_2_2_44_1","volume-title":"Graphadaptive Rectified Linear Unit for Graph Neural Networks. In WWW '22: The ACM Web Conference 2022","author":"Zhang Yifei","year":"2022","unstructured":"Yifei Zhang, Hao Zhu, Ziqiao Meng, Piotr Koniusz, and Irwin King. 2022. Graphadaptive Rectified Linear Unit for Graph Neural Networks. In WWW '22: The ACM Web Conference 2022, Virtual Event, Lyon, France, April 25 - 29, 2022. ACM, 1331--1339."},{"key":"e_1_3_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482168"},{"key":"e_1_3_2_2_46_1","volume-title":"Simple Spectral Graph Convolution. In International Conference on Learning Representations.","author":"Zhu Hao","year":"2021","unstructured":"Hao Zhu and Piotr Koniusz. 2021. Simple Spectral Graph Convolution. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_47_1","volume-title":"Contrastive Laplacian Eigenmaps. Advances in Neural Information Processing Systems 34","author":"Zhu Hao","year":"2021","unstructured":"Hao Zhu, Ke Sun, and Peter Koniusz. 2021. Contrastive Laplacian Eigenmaps. Advances in Neural Information Processing Systems 34 (2021)."},{"key":"e_1_3_2_2_48_1","volume-title":"Deep graph contrastive representation learning. arXiv preprint arXiv:2006.04131","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. arXiv preprint arXiv:2006.04131 (2020)."},{"key":"e_1_3_2_2_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449802"}],"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\/3534678.3539425","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3534678.3539425","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:03:03Z","timestamp":1750186983000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3534678.3539425"}},"subtitle":["Covariance-Preserving Feature Augmentation for Graph Contrastive Learning"],"short-title":[],"issued":{"date-parts":[[2022,8,14]]},"references-count":48,"alternative-id":["10.1145\/3534678.3539425","10.1145\/3534678"],"URL":"https:\/\/doi.org\/10.1145\/3534678.3539425","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"}}]}}