Recent Advances in Reliable Deep Graph Learning: Inherent Noise,
Distribution Shift, and Adversarial Attack

Recent Advances in Reliable Deep Graph Learning: Inherent Noise, Distribution Shift, and Adversarial Attack

15 February 2022

Papers citing "Recent Advances in Reliable Deep Graph Learning: Inherent Noise, Distribution Shift, and Adversarial Attack"

9 / 9 papers shown

Title
Framework GNN-AID: Graph Neural Network Analysis Interpretation and Defense Kirill Lukyanov Mikhail Drobyshevskiy Georgii Sazonov Mikhail Soloviov Ilya Makarov GNN 34 0 0 06 May 2025
A Survey of Graph Neural Networks in Real world: Imbalance, Noise, Privacy and OOD Challenges Wei Ju Siyu Yi Yifan Wang Zhiping Xiao Zhengyan Mao ... Senzhang Wang Xinwang Liu Xiao Luo Philip S. Yu Ming Zhang AI4CE 34 36 0 07 Mar 2024
Uncertainty-Aware Robust Learning on Noisy Graphs Shuyi Chen Kaize Ding Shixiang Zhu 11 5 0 14 Jun 2023
Less Can Be More: Unsupervised Graph Pruning for Large-scale Dynamic Graphs Jintang Li Sheng Tian Ruofan Wu Liang Zhu Welong Zhao Changhua Meng Liang Chen Zibin Zheng Hongzhi Yin 18 10 0 18 May 2023
Empowering Graph Representation Learning with Test-Time Graph Transformation Wei Jin Tong Zhao Jiayu Ding Yozen Liu Jiliang Tang Neil Shah OOD 84 59 0 07 Oct 2022
DrugOOD: Out-of-Distribution (OOD) Dataset Curator and Benchmark for AI-aided Drug Discovery -- A Focus on Affinity Prediction Problems with Noise Annotations Yuanfeng Ji Lu Zhang Jiaxiang Wu Bing Wu Long-Kai Huang ... Ping Luo Shuigeng Zhou Junzhou Huang Peilin Zhao Yatao Bian OOD 54 73 0 24 Jan 2022
Be Confident! Towards Trustworthy Graph Neural Networks via Confidence Calibration Xiao Wang Hongrui Liu Chuan Shi Cheng Yang UQCV 93 113 0 29 Sep 2021
Elastic Graph Neural Networks Xiaorui Liu W. Jin Yao Ma Yaxin Li Hua Liu Yiqi Wang Ming Yan Jiliang Tang 84 106 0 05 Jul 2021
MoleculeNet: A Benchmark for Molecular Machine Learning Zhenqin Wu Bharath Ramsundar Evan N. Feinberg Joseph Gomes C. Geniesse Aneesh S. Pappu K. Leswing Vijay S. Pande OOD 154 1,748 0 02 Mar 2017