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Improving Generalizability of Graph Anomaly Detection Models via Data Augmentation


Sep 21, 2022
Shuang Zhou, Xiao Huang, Ninghao Liu, Fu-Lai Chung, Long-Kai Huang


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Towards Memory Efficient Training via Dual Activation Precision


Aug 05, 2022
Guanchu Wang, Zirui Liu, Zhimeng Jiang, Ninghao Liu, Na Zou, Xia Hu


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Mitigating Algorithmic Bias with Limited Annotations


Jul 20, 2022
Guanchu Wang, Mengnan Du, Ninghao Liu, Na Zou, Xia Hu


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G-Mixup: Graph Data Augmentation for Graph Classification


Feb 16, 2022
Xiaotian Han, Zhimeng Jiang, Ninghao Liu, Xia Hu


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Geometric Graph Representation Learning via Maximizing Rate Reduction


Feb 13, 2022
Xiaotian Han, Zhimeng Jiang, Ninghao Liu, Qingquan Song, Jundong Li, Xia Hu

* Accepted by TheWebConference(WWW) 2022 

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MGAE: Masked Autoencoders for Self-Supervised Learning on Graphs


Jan 07, 2022
Qiaoyu Tan, Ninghao Liu, Xiao Huang, Rui Chen, Soo-Hyun Choi, Xia Hu


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Defense Against Explanation Manipulation


Nov 08, 2021
Ruixiang Tang, Ninghao Liu, Fan Yang, Na Zou, Xia Hu


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Modeling Techniques for Machine Learning Fairness: A Survey


Nov 04, 2021
Mingyang Wan, Daochen Zha, Ninghao Liu, Na Zou

* 21 pages, 4 figures 

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Adaptive Label Smoothing To Regularize Large-Scale Graph Training


Aug 30, 2021
Kaixiong Zhou, Ninghao Liu, Fan Yang, Zirui Liu, Rui Chen, Li Li, Soo-Hyun Choi, Xia Hu


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EDITS: Modeling and Mitigating Data Bias for Graph Neural Networks


Aug 11, 2021
Yushun Dong, Ninghao Liu, Brian Jalaian, Jundong Li


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