{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,26]],"date-time":"2025-06-26T15:04:03Z","timestamp":1750950243386,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":58,"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:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/https:\/\/doi.org\/10.13039\/100000001","name":"NSF (National Science Foundation)","doi-asserted-by":"publisher","award":["IIS 2340346, CMMI-2146076, IIS 2334193"],"award-info":[{"award-number":["IIS 2340346, CMMI-2146076, IIS 2334193"]}],"id":[{"id":"10.13039\/https:\/\/doi.org\/10.13039\/100000001","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\/3637528.3671830","type":"proceedings-article","created":{"date-parts":[[2024,8,25]],"date-time":"2024-08-25T04:54:55Z","timestamp":1724561695000},"page":"4083-4094","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Graph Cross Supervised Learning via Generalized Knowledge"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5270-4817","authenticated-orcid":false,"given":"Xiangchi","family":"Yuan","sequence":"first","affiliation":[{"name":"Brandeis University &amp; Georgia Institute of Technology, Waltham, MA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2795-6080","authenticated-orcid":false,"given":"Yijun","family":"Tian","sequence":"additional","affiliation":[{"name":"University of Notre Dame, South Bend, IN, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6380-3340","authenticated-orcid":false,"given":"Chunhui","family":"Zhang","sequence":"additional","affiliation":[{"name":"Dartmouth College, Hanover, NH, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6038-2173","authenticated-orcid":false,"given":"Yanfang","family":"Ye","sequence":"additional","affiliation":[{"name":"University of Notre Dame, South Bend, IN, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3932-5956","authenticated-orcid":false,"given":"Nitesh V.","family":"Chawla","sequence":"additional","affiliation":[{"name":"University of Notre Dame, South Bend, IN, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8349-7926","authenticated-orcid":false,"given":"Chuxu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Brandeis University, Waltham, MA, USA"}]}],"member":"320","published-online":{"date-parts":[[2024,8,24]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Relational inductive biases, deep learning, and graph networks. arXiv preprint arXiv:1806.01261","author":"Battaglia Peter W","year":"2018","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. Relational inductive biases, deep learning, and graph networks. arXiv preprint arXiv:1806.01261, 2018."},{"key":"e_1_3_2_2_2_1","volume-title":"NeurIPS","author":"Chen Junjie","year":"2021","unstructured":"Junjie Chen, Li Niu, Liu Liu, and Liqing Zhang. Weak-shot fine-grained classification via similarity transfer. In NeurIPS, 2021."},{"key":"e_1_3_2_2_3_1","volume-title":"Efflex: Efficient and flexible pipeline for spatio-temporal trajectory graph modeling and representation learning. arXiv preprint arXiv:2404.12400","author":"Cheng Ming","year":"2024","unstructured":"Ming Cheng, Ziyi Zhou, Bowen Zhang, Ziyu Wang, Jiaqi Gan, Ziang Ren, Weiqi Feng, Yi Lyu, Hefan Zhang, and Xingjian Diao. Efflex: Efficient and flexible pipeline for spatio-temporal trajectory graph modeling and representation learning. arXiv preprint arXiv:2404.12400, 2024."},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467364"},{"key":"e_1_3_2_2_5_1","volume-title":"WWW","author":"Fan Wenqi","year":"2019","unstructured":"Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, and Dawei Yin. Graph neural networks for social recommendation. In WWW, 2019."},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.emnlp-main.201"},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219947"},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939754"},{"key":"e_1_3_2_2_9_1","volume-title":"NeurIPS","author":"Hamilton William L.","year":"2017","unstructured":"William L. Hamilton, Rex Ying, and Jure Leskovec. Inductive representation learning on large graphs. In NeurIPS, 2017."},{"key":"e_1_3_2_2_10_1","volume-title":"NeurIPS","author":"Hoffman Judy","year":"2014","unstructured":"Judy Hoffman, Sergio Guadarrama, Eric S Tzeng, Ronghang Hu, Jeff Donahue, Ross Girshick, Trevor Darrell, and Kate Saenko. Lsda: Large scale detection through adaptation. In NeurIPS, 2014."},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00445"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467408"},{"key":"e_1_3_2_2_13_1","volume-title":"ICLR","author":"Jia Yaning","year":"2024","unstructured":"Yaning Jia, Chunhui Zhang, and Soroush Vosoughi. Aligning relational learning with lipschitz fairness. In ICLR, 2024."},{"key":"e_1_3_2_2_14_1","volume-title":"International Conference on Machine Learning","author":"Kim Hyunjik","year":"2021","unstructured":"Hyunjik Kim, George Papamakarios, and Andriy Mnih. The lipschitz constant of self-attention. In International Conference on Machine Learning, 2021."},{"key":"e_1_3_2_2_15_1","volume-title":"ICLR","author":"Diederik","year":"2015","unstructured":"Diederik P. Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In ICLR, 2015."},{"key":"e_1_3_2_2_16_1","volume-title":"ICLR","author":"Thomas","year":"2017","unstructured":"Thomas N. Kipf and Max Welling. Semi-supervised classification with graph convolutional networks. In ICLR, 2017."},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00930"},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00936"},{"key":"e_1_3_2_2_19_1","volume-title":"Abdulellah Abualshour, Ali Kassem Thabet, and Bernard Ghanem. Deepgcns: Making gcns go as deep as cnns","author":"Li Guohao","year":"2021","unstructured":"Guohao Li, Matthias M\u00fcller, Guocheng Qian, Itzel Carolina Delgadillo Perez, Abdulellah Abualshour, Ali Kassem Thabet, and Bernard Ghanem. Deepgcns: Making gcns go as deep as cnns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021."},{"key":"e_1_3_2_2_20_1","volume-title":"ICML","author":"Li Guohao","year":"2021","unstructured":"Guohao Li, Matthias M\u00fcller, Bernard Ghanem, and Vladlen Koltun. Training graph neural networks with 1000 layers. In ICML, 2021."},{"key":"e_1_3_2_2_21_1","volume-title":"NeurIPS","author":"Liu Yan","year":"2021","unstructured":"Yan Liu, Zhijie Zhang, Li Niu, Junjie Chen, and Liqing Zhang. Mixed supervised object detection by transferring mask prior and semantic similarity. In NeurIPS, 2021."},{"key":"e_1_3_2_2_22_1","volume-title":"WWW","author":"Liu Zheyuan","year":"2023","unstructured":"Zheyuan Liu, Chunhui Zhang, Yijun Tian, Erchi Zhang, Chao Huang, Yanfang Ye, and Chuxu Zhang. Fair graph representation learning via diverse mixture-of-experts. WWW, 2023."},{"key":"e_1_3_2_2_23_1","volume-title":"WWW","author":"Myers Seth A","year":"2014","unstructured":"Seth A Myers, Aneesh Sharma, Pankaj Gupta, and Jimmy Lin. Information network or social network? the structure of the twitter follow graph. In WWW, 2014."},{"key":"e_1_3_2_2_24_1","volume-title":"Choong Jun Jin, and Tsuyoshi Murata. Learning graph neural networks with noisy labels. arXiv preprint arXiv:1905.01591","author":"Hoang","year":"2019","unstructured":"Hoang NT, Choong Jun Jin, and Tsuyoshi Murata. Learning graph neural networks with noisy labels. arXiv preprint arXiv:1905.01591, 2019."},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1609\/icwsm.v18i1.31379"},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.240"},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3539597.3570369"},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2020.3033962"},{"key":"e_1_3_2_2_29_1","volume-title":"ICLR","author":"Shazeer Noam","year":"2017","unstructured":"Noam Shazeer, *Azalia Mirhoseini, *Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, and Jeff Dean. Outrageously large neural networks: The sparsely-gated mixture-of-experts layer. In ICLR, 2017."},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539249"},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i8.26192"},{"key":"e_1_3_2_2_32_1","volume-title":"ICLR","author":"Tian Yijun","year":"2023","unstructured":"Yijun Tian, Chuxu Zhang, Zhichun Guo, Xiangliang Zhang, and Nitesh V Chawla. Learning mlps on graphs: A unified view of effectiveness, robustness, and efficiency. In ICLR, 2023."},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i17.29875"},{"key":"e_1_3_2_2_34_1","volume-title":"ICLR","author":"Velickovic Petar","year":"2018","unstructured":"Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Li\u00f2, and Yoshua Bengio. Graph attention networks. In ICLR, 2018."},{"key":"e_1_3_2_2_35_1","volume-title":"NeurIPS","author":"Virmaux Aladin","year":"2018","unstructured":"Aladin Virmaux and Kevin Scaman. Lipschitz regularity of deep neural networks: analysis and efficient estimation. In NeurIPS, 2018."},{"key":"e_1_3_2_2_36_1","volume-title":"NeurIPS","author":"Wang Song","year":"2022","unstructured":"Song Wang, Chen Chen, and Jundong Li. Graph few-shot learning with task-specific structures. NeurIPS, 2022."},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539265"},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599288"},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330989"},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.3390\/informatics10010008"},{"key":"e_1_3_2_2_41_1","volume-title":"The 40th Conference on Uncertainty in Artificial Intelligence","author":"Wen Qianlong","year":"2024","unstructured":"Qianlong Wen, Zhongyu Ouyang, Chunhui Zhang, Yiyue Qian, Chuxu Zhang, and Yanfang Ye. GCVR: Reconstruction from cross-view enable sufficient and robust graph contrastive learning. In The 40th Conference on Uncertainty in Artificial Intelligence, 2024."},{"key":"e_1_3_2_2_42_1","volume-title":"ICML","author":"Wu Felix","year":"2019","unstructured":"Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Weinberger. Simplifying graph convolutional networks. In ICML, 2019."},{"key":"e_1_3_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/669"},{"key":"e_1_3_2_2_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"e_1_3_2_2_45_1","volume-title":"NeurIPS","author":"Ying Zhitao","year":"2018","unstructured":"Zhitao Ying, Jiaxuan You, Christopher Morris, Xiang Ren, Will Hamilton, and Jure Leskovec. Hierarchical graph representation learning with differentiable pooling. In NeurIPS, 2018."},{"key":"e_1_3_2_2_46_1","volume-title":"NeurIPS","author":"You Yuning","year":"2020","unstructured":"Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen. Graph contrastive learning with augmentations. In NeurIPS, 2020."},{"key":"e_1_3_2_2_47_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i10.21417"},{"key":"e_1_3_2_2_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3531937"},{"key":"e_1_3_2_2_49_1","volume-title":"The Second Workshop on New Frontiers in Adversarial Machine Learning","author":"Yuan Xiangchi","year":"2023","unstructured":"Xiangchi Yuan, Chunhui Zhang, Yijun Tian, and Chuxu Zhang. Navigating graph robust learning against all-intensity attacks. In The Second Workshop on New Frontiers in Adversarial Machine Learning, 2023."},{"key":"e_1_3_2_2_50_1","volume-title":"ICLR","author":"Yuan Xiangchi","year":"2024","unstructured":"Xiangchi Yuan, Chunhui Zhang, Yijun Tian, Yanfang Ye, and Chuxu Zhang. Mitigating emergent robustness degradation while scaling graph learning. In ICLR, 2024."},{"key":"e_1_3_2_2_51_1","volume-title":"ICML","author":"Zhang Chunhui","year":"2023","unstructured":"Chunhui Zhang, Chao Huang, Yijun Tian, Qianlong Wen, Zhongyu Ouyang, Youhuan Li, Yanfang Ye, and Chuxu Zhang. When sparsity meets contrastive models: Less graph data can bring better class-balanced representations. In ICML, 2023."},{"key":"e_1_3_2_2_52_1","volume-title":"ICLR","author":"Zhang Chunhui","year":"2023","unstructured":"Chunhui Zhang, Yijun Tian, Mingxuan Ju, Zheyuan Liu, Yanfang Ye, Nitesh Chawla, and Chuxu Zhang. Chasing all-round graph representation robustness: Model, training, and optimization. In ICLR, 2023."},{"key":"e_1_3_2_2_53_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330961"},{"key":"e_1_3_2_2_54_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM50108.2020.00088"},{"key":"e_1_3_2_2_55_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539415"},{"key":"e_1_3_2_2_56_1","volume-title":"WWW","author":"Zheng Da","year":"2020","unstructured":"Da Zheng, Minjie Wang, Quan Gan, Zheng Zhang, and George Karypis. Learning graph neural networks with deep graph library. In WWW, 2020."},{"key":"e_1_3_2_2_57_1","volume-title":"NeurIPS","author":"Zheng Qinkai","year":"2021","unstructured":"Qinkai Zheng, Xu Zou, Yuxiao Dong, Yukuo Cen, Da Yin, Jiarong Xu, Yang Yang, and Jie Tang. Graph robustness benchmark: Benchmarking the adversarial robustness of graph machine learning. In NeurIPS, 2021."},{"key":"e_1_3_2_2_58_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58574-7_37"}],"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\/3637528.3671830","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3637528.3671830","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:04:14Z","timestamp":1750291454000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3637528.3671830"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,24]]},"references-count":58,"alternative-id":["10.1145\/3637528.3671830","10.1145\/3637528"],"URL":"https:\/\/doi.org\/10.1145\/3637528.3671830","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"}}]}}