{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T00:23:32Z","timestamp":1788740612505,"version":"build-2803163510"},"publisher-location":"New York, NY, USA","reference-count":41,"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":"National Natural Science Foundation of China","award":["61976204, U1811461, U1836206"],"award-info":[{"award-number":["61976204, U1811461, U1836206"]}]},{"name":"Beijing Nova Program","award":["Z201100006820062"],"award-info":[{"award-number":["Z201100006820062"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,8,14]]},"DOI":"10.1145\/3534678.3539483","type":"proceedings-article","created":{"date-parts":[[2022,8,12]],"date-time":"2022-08-12T19:06:41Z","timestamp":1660331201000},"page":"1131-1140","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":20,"title":["UD-GNN"],"prefix":"10.1145","author":[{"given":"Yang","family":"Liu","sequence":"first","affiliation":[{"name":"Institute of Computing Technology, CAS & University of Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiang","family":"Ao","sequence":"additional","affiliation":[{"name":"Institute of Computing Technology, CAS & University of Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fuli","family":"Feng","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qing","family":"He","sequence":"additional","affiliation":[{"name":"Institute of Computing Technology, CAS & University of Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,8,14]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing. In ICML. PMLR, 21--29.","author":"Abu-El-Haija Sami","year":"2019","unstructured":"Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Nazanin Alipourfard, et al. 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":"Aleksandar Bojchevski and Stephan G\u00fcnnemann. 2018. Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking. In ICLR."},{"key":"e_1_3_2_2_3_1","unstructured":"Eli Chien Jianhao Peng Pan Li and Olgica Milenkovic. 2020. Adaptive universal generalized pagerank graph neural network. In ICLR."},{"key":"e_1_3_2_2_4_1","unstructured":"Yash Deshpande Andrea Montanari Elchanan Mossel and Subhabrata Sen. 2018. Contextual stochastic block models. In NeurIPS."},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"crossref","unstructured":"Boyuan Feng Yuke Wang and Yufei Ding. 2021 b. UAG: Uncertainty-aware Attention Graph Neural Network for Defending Adversarial Attacks. In AAAI.","DOI":"10.1609\/aaai.v35i8.16908"},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"crossref","unstructured":"Fuli Feng Weiran Huang Xiangnan He Xin Xin Qifan Wang and Tat-Seng Chua. 2021 a. Should graph convolution trust neighbors? a simple causal inference method. In SIGIR. 1208--1218.","DOI":"10.1145\/3404835.3462971"},{"key":"e_1_3_2_2_7_1","unstructured":"Guoji Fu Yifan Hou et al. 2020. Understanding graph neural networks from graph signal denoising perspectives. arXiv preprint arXiv:2006.04386 (2020)."},{"key":"e_1_3_2_2_8_1","unstructured":"Yarin Gal and Zoubin Ghahramani. 2016. Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In ICML. PMLR 1050--1059."},{"key":"e_1_3_2_2_9_1","volume-title":"NeurIPS Workshop Neuro AI","author":"Golkar Siavash","year":"2019","unstructured":"Siavash Golkar, Michael Kagan, and Kyunghyun Cho. 2019. Continual learning via neural pruning. NeurIPS Workshop Neuro AI (2019)."},{"key":"e_1_3_2_2_10_1","volume-title":"Hongzhi Chen, and Ming-Chang Yang.","author":"Hou Yifan","year":"2020","unstructured":"Yifan Hou, Jian Zhang, James Cheng, Kaili Ma, Richard TB Ma, Hongzhi Chen, and Ming-Chang Yang. 2020. Measuring and improving the use of graph information in graph neural networks. In ICLR."},{"key":"e_1_3_2_2_11_1","volume-title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs. arXiv preprint arXiv:2005.00687","author":"Hu Weihua","year":"2020","unstructured":"Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec. 2020. Open Graph Benchmark: Datasets for Machine Learning on Graphs. arXiv preprint arXiv:2005.00687 (2020)."},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"crossref","unstructured":"Chao Huang Huance Xu Yong Xu Peng Dai Lianghao Xiao Mengyin Lu Liefeng Bo Hao Xing Xiaoping Lai and Yanfang Ye. 2021 b. Knowledge-aware coupled graph neural network for social recommendation. In AAAI.","DOI":"10.1609\/aaai.v35i5.16533"},{"key":"e_1_3_2_2_13_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. 1311--1321.","DOI":"10.1145\/3485447.3512178"},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"crossref","unstructured":"Tinglin Huang Yuxiao Dong Ming Ding Zhen Yang Wenzheng Feng Xinyu Wang and Jie Tang. 2021 a. MixGCF: An Improved Training Method for Graph Neural Network-based Recommender Systems. In KDD. 665--674.","DOI":"10.1145\/3447548.3467408"},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"crossref","unstructured":"Yongxiang Huang and Albert CS Chung. 2020. Edge-variational graph convolutional networks for uncertainty-aware disease prediction. In MICCAI.","DOI":"10.1007\/978-3-030-59728-3_55"},{"key":"e_1_3_2_2_16_1","volume-title":"NeurIPS","volume":"34","author":"Jin Di","year":"2021","unstructured":"Di Jin, Zhizhi Yu, Cuiying Huo, Rui Wang, Xiao Wang, Dongxiao He, and Jiawei Han. 2021. Universal Graph Convolutional Networks. NeurIPS, Vol. 34 (2021)."},{"key":"e_1_3_2_2_17_1","first-page":"3647","article-title":"Continual learning with node-importance based adaptive group sparse regularization","volume":"33","author":"Jung Sangwon","year":"2020","unstructured":"Sangwon Jung, Hongjoon Ahn, Sungmin Cha, and Taesup Moon. 2020. Continual learning with node-importance based adaptive group sparse regularization. NeurIPS, Vol. 33 (2020), 3647--3658.","journal-title":"NeurIPS"},{"key":"e_1_3_2_2_18_1","volume-title":"NeurIPS","volume":"30","author":"Kendall Alex","year":"2017","unstructured":"Alex Kendall and Yarin Gal. 2017. What uncertainties do we need in bayesian deep learning for computer vision? NeurIPS, Vol. 30 (2017)."},{"key":"e_1_3_2_2_19_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_20_1","volume-title":"Vaishnavi Gupta, Omkar Bhalerao, and Ser Nam Lim.","author":"Lim Derek","year":"2021","unstructured":"Derek Lim, Felix Hohne, Xiuyu Li, Sijia Linda Huang, Vaishnavi Gupta, Omkar Bhalerao, and Ser Nam Lim. 2021. Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods. NeurIPS, Vol. 34 (2021)."},{"key":"e_1_3_2_2_21_1","volume-title":"et almbox","author":"Liu Shengchao","year":"2022","unstructured":"Shengchao Liu, Hanchen Wang, Weiyang Liu, Joan Lasenby, et almbox. 2022. Pre-training Molecular Graph Representation with 3D Geometry. In ICLR."},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449989"},{"key":"e_1_3_2_2_23_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_24_1","volume-title":"Revisiting graph neural networks: All we have is low-pass filters. arXiv preprint arXiv:1905.09550","author":"Nt Hoang","year":"2019","unstructured":"Hoang Nt and Takanori Maehara. 2019. Revisiting graph neural networks: All we have is low-pass filters. arXiv preprint arXiv:1905.09550 (2019)."},{"key":"e_1_3_2_2_25_1","volume-title":"Yu Lei, and Bo Yang.","author":"Pei Hongbin","year":"2020","unstructured":"Hongbin Pei, Bingzhe Wei, Kevin Chen-Chuan Chang, Yu Lei, and Bo Yang. 2020. Geom-gcn: Geometric graph convolutional networks. In ICLR."},{"key":"e_1_3_2_2_26_1","volume-title":"Homophily-heterophily: Relational concepts for communication research. Public opinion quarterly","author":"Rogers Everett M","year":"1970","unstructured":"Everett M Rogers and Dilip K Bhowmik. 1970. Homophily-heterophily: Relational concepts for communication research. Public opinion quarterly, Vol. 34, 4 (1970)."},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1093\/comnet\/cnab014"},{"key":"e_1_3_2_2_28_1","volume-title":"An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747","author":"Ruder Sebastian","year":"2016","unstructured":"Sebastian Ruder. 2016. An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747 (2016)."},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2020.01.021"},{"key":"e_1_3_2_2_30_1","unstructured":"Otilia Stretcu Krishnamurthy Viswanathan et al. 2019. Graph agreement models for semi-supervised learning. In NeurIPS."},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"crossref","unstructured":"Susheel Suresh Vinith Budde Jennifer Neville Pan Li and Jianzhu Ma. 2021. Breaking the Limit of Graph Neural Networks by Improving the Assortativity of Graphs with Local Mixing Patterns. In KDD.","DOI":"10.1145\/3447548.3467373"},{"key":"e_1_3_2_2_32_1","volume-title":"Social structure of Facebook networks. Physica A: Statistical Mechanics and its Applications","author":"Traud Amanda L","year":"2012","unstructured":"Amanda L Traud, Peter J Mucha, and Mason A Porter. 2012. Social structure of Facebook networks. Physica A: Statistical Mechanics and its Applications (2012)."},{"key":"e_1_3_2_2_33_1","unstructured":"Petar Velivc kovi\u0107 Guillem Cucurull Arantxa Casanova Adriana Romero Pietro Li\u00f2 and Yoshua Bengio. 2018. Graph Attention Networks. In ICLR."},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1162\/qss_a_00021"},{"key":"e_1_3_2_2_35_1","unstructured":"Minjie Wang and Da Zheng et al. 2019. Deep Graph Library: A Graph-Centric Highly-Performant Package for Graph Neural Networks. arXiv preprint (2019)."},{"key":"e_1_3_2_2_36_1","unstructured":"Keyulu Xu Chengtao Li Yonglong Tian Tomohiro Sonobe Ken-ichi Kawarabayashi and Stefanie Jegelka. 2018. Representation learning on graphs with jumping knowledge networks. In ICML. PMLR 5453--5462."},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"crossref","unstructured":"Li Zhang and Haiping Lu. 2020. A Feature-Importance-Aware and Robust Aggregator for GCN. In CIKM. 1813--1822.","DOI":"10.1145\/3340531.3411983"},{"key":"e_1_3_2_2_38_1","first-page":"12827","article-title":"Uncertainty aware semi-supervised learning on graph data","volume":"33","author":"Zhao Xujiang","year":"2020","unstructured":"Xujiang Zhao, Feng Chen, Shu Hu, and Jin-Hee Cho. 2020. Uncertainty aware semi-supervised learning on graph data. NeurIPS, Vol. 33 (2020), 12827--12836.","journal-title":"NeurIPS"},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"crossref","unstructured":"Qiwei Zhong Yang Liu Xiang Ao Binbin Hu Jinghua Feng Jiayu Tang and Qing He. 2020. Financial defaulter detection on online credit payment via multi-view attributed heterogeneous information network. In WWW. 785--795.","DOI":"10.1145\/3366423.3380159"},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"crossref","unstructured":"Jiong Zhu Ryan A Rossi Anup Rao Tung Mai Nedim Lipka Nesreen K Ahmed and Danai Koutra. 2021. Graph Neural Networks with Heterophily. In AAAI.","DOI":"10.1609\/aaai.v35i12.17332"},{"key":"e_1_3_2_2_41_1","unstructured":"Jiong Zhu Yujun Yan Lingxiao Zhao Mark Heimann Leman Akoglu and Danai Koutra. 2020. Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs. In NeurIPS."}],"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\/3534678.3539483","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3534678.3539483","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\/3534678.3539483"}},"subtitle":["Uncertainty-aware Debiased Training on Semi-Homophilous Graphs"],"short-title":[],"issued":{"date-parts":[[2022,8,14]]},"references-count":41,"alternative-id":["10.1145\/3534678.3539483","10.1145\/3534678"],"URL":"https:\/\/doi.org\/10.1145\/3534678.3539483","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"}}]}}