Does Invariant Graph Learning via Environment Augmentation Learn Invariance?

29 October 2023

Bo Han

Papers citing "Does Invariant Graph Learning via Environment Augmentation Learn Invariance?"

39 / 39 papers shown

Title
Learning Repetition-Invariant Representations for Polymer Informatics Yihan Zhu Gang Liu Eric Inae Tengfei Luo Meng Jiang 7 0 0 15 May 2025
Soft causal learning for generalized molecule property prediction: An environment perspective Limin Li Kuo Yang Wenjie Du Pengkun Wang Zhengyang Zhou Yang Wang OOD AI4CE 56 0 0 07 May 2025
Out-of-Distribution Generalization on Graphs via Progressive Inference Yiming Xu Bin Shi Zhen Peng Huixiang Liu Bo Dong Chen Chen OOD AI4CE 76 0 0 04 Mar 2025
Generative Risk Minimization for Out-of-Distribution Generalization on Graphs Song Wang Zhen Tan Yaochen Zhu Chuxu Zhang Wenlin Yao OOD 101 0 0 11 Feb 2025
BrainOOD: Out-of-distribution Generalizable Brain Network Analysis Jiaxing Xu Yongqiang Chen Xia Dong Mengcheng Lan Tiancheng Huang Qingtian Bian James Cheng Litong Feng OOD 68 0 0 02 Feb 2025
Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization Song Wang Xiaodong Yang Rashidul Islam Huiyuan Chen Minghua Xu Jundong Li Yiwei Cai OODD 52 2 0 07 Jan 2025
Scale Invariance of Graph Neural Networks Qin Jiang Chengjia Wang Michael Lones Wei Pang GNN 112 0 0 28 Nov 2024
Subgraph Aggregation for Out-of-Distribution Generalization on Graphs Bowen Liu Haoyang Li Shuning Wang Shuo Nie Shanghang Zhang OODD CML 71 0 0 29 Oct 2024
A Survey of Deep Graph Learning under Distribution Shifts: from Graph Out-of-Distribution Generalization to Adaptation Kexin Zhang Shuhan Liu Song Wang Weili Shi Chen Chen Pan Li Sheng Li Jundong Li Kaize Ding OOD 47 2 0 25 Oct 2024
Mitigating Graph Covariate Shift via Score-based Out-of-distribution Augmentation Bohan Wang Yurui Chang Lu Lin OODD OOD 37 0 0 23 Oct 2024
Leveraging Invariant Principle for Heterophilic Graph Structure Distribution Shifts Jinluan Yang Zhengyu Chen Teng Xiao Wenqiao Zhang Yong Lin Kun Kuang 53 1 0 18 Aug 2024
Invariant Graph Learning Meets Information Bottleneck for Out-of-Distribution Generalization Wenyu Mao Jiancan Wu Haoyang Liu Yongduo Sui Xiang Wang OOD 39 2 0 03 Aug 2024
Unifying Invariant and Variant Features for Graph Out-of-Distribution via Probability of Necessity and Sufficiency Xuexin Chen Ruichu Cai Kaitao Zheng Zhifan Jiang Zhengting Huang Zhifeng Hao Zijian Li 54 0 0 21 Jul 2024
Improving Graph Out-of-distribution Generalization on Real-world Data Can Xu Yao Cheng Jianxiang Yu Haosen Wang Jingsong Lv Xiang Li OOD 34 0 0 14 Jul 2024
Empowering Graph Invariance Learning with Deep Spurious Infomax Tianjun Yao Yongqiang Chen Zhenhao Chen Kai Hu Zhiqiang Shen Anton van den Hengel OOD 39 6 0 13 Jul 2024
How Interpretable Are Interpretable Graph Neural Networks? Yongqiang Chen Yatao Bian Bo Han James Cheng 49 4 0 12 Jun 2024
Improving out-of-distribution generalization in graphs via hierarchical semantic environments Yinhua Piao Sangseon Lee Yijingxiu Lu Sun Kim OOD 34 2 0 04 Mar 2024
Pairwise Alignment Improves Graph Domain Adaptation Shikun Liu Deyu Zou Han Zhao Pan Li OOD 40 7 0 02 Mar 2024
Unifying Invariance and Spuriousity for Graph Out-of-Distribution via Probability of Necessity and Sufficiency Xuexin Chen Ruichu Cai Kaitao Zheng Zhifan Jiang Zhengting Huang Zhifeng Hao Zijian Li 24 2 0 14 Feb 2024
Enhancing Evolving Domain Generalization through Dynamic Latent Representations Binghui Xie Yongqiang Chen Jiaqi Wang Kaiwen Zhou Bo Han Wei Meng James Cheng 41 5 0 16 Jan 2024
When Graph Neural Network Meets Causality: Opportunities, Methodologies and An Outlook Wenzhao Jiang Hao Liu Hui Xiong CML AI4CE 49 2 0 19 Dec 2023
Neural Atoms: Propagating Long-range Interaction in Molecular Graphs through Efficient Communication Channel Xuan Li Zhanke Zhou Jiangchao Yao Yu Rong Lu Zhang Bo Han 37 3 0 02 Nov 2023
Causality and Independence Enhancement for Biased Node Classification Guoxin Chen Yongqing Wang Fangda Guo Qinglang Guo Jiangli Shao Huawei Shen Xueqi Cheng CML AI4CE OOD 37 14 0 14 Oct 2023
Towards out-of-distribution generalizable predictions of chemical kinetics properties Zihao Wang Yongqiang Chen Yang Duan Weijiang Li Bo Han James Cheng Hanghang Tong OOD 28 6 0 04 Oct 2023
Discovering environments with XRM Mohammad Pezeshki Diane Bouchacourt Mark Ibrahim Jimuyang Zhang Pascal Vincent David Lopez-Paz 43 18 0 28 Sep 2023
Joint Learning of Label and Environment Causal Independence for Graph Out-of-Distribution Generalization Shurui Gui Meng Liu Xiner Li Youzhi Luo Shuiwang Ji CML OOD 23 24 0 01 Jun 2023
Mind the Label Shift of Augmentation-based Graph OOD Generalization Junchi Yu Jian Liang Ran He 34 27 0 27 Mar 2023
Towards Better Out-of-Distribution Generalization of Neural Algorithmic Reasoning Tasks Sadegh Mahdavi Kevin Swersky Thomas Kipf Milad Hashemi Christos Thrampoulidis Renjie Liao LRM OOD NAI 45 25 0 01 Nov 2022
Empowering Graph Representation Learning with Test-Time Graph Transformation Wei Jin Tong Zhao Jiayu Ding Yozen Liu Jiliang Tang Neil Shah OOD 86 60 0 07 Oct 2022
Towards Better Generalization with Flexible Representation of Multi-Module Graph Neural Networks Hyungeun Lee Kijung Yoon AI4CE 28 2 0 14 Sep 2022
OOD Link Prediction Generalization Capabilities of Message-Passing GNNs in Larger Test Graphs Yangze Zhou Gitta Kutyniok Bruno Ribeiro OODD AI4CE 73 37 0 30 May 2022
Discovering Invariant Rationales for Graph Neural Networks Yingmin Wu Xiang Wang An Zhang Xiangnan He Tat-Seng Chua OOD AI4CE 99 224 0 30 Jan 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 63 73 0 24 Jan 2022
CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks Ana Lucic Maartje ter Hoeve Gabriele Tolomei Maarten de Rijke Fabrizio Silvestri 115 142 0 05 Feb 2021
Does Invariant Risk Minimization Capture Invariance? Pritish Kamath Akilesh Tangella Danica J. Sutherland Nathan Srebro OOD 196 125 0 04 Jan 2021
Explainability in Graph Neural Networks: A Taxonomic Survey Hao Yuan Haiyang Yu Shurui Gui Shuiwang Ji 167 592 0 31 Dec 2020
From Local Structures to Size Generalization in Graph Neural Networks Gilad Yehudai Ethan Fetaya E. Meirom Gal Chechik Haggai Maron GNN AI4CE 169 123 0 17 Oct 2020
Out-of-Distribution Generalization via Risk Extrapolation (REx) David M. Krueger Ethan Caballero J. Jacobsen Amy Zhang Jonathan Binas Dinghuai Zhang Rémi Le Priol Aaron Courville OOD 215 901 0 02 Mar 2020
Representation Learning on Graphs with Jumping Knowledge Networks Keyulu Xu Chengtao Li Yonglong Tian Tomohiro Sonobe Ken-ichi Kawarabayashi Stefanie Jegelka GNN 267 1,945 0 09 Jun 2018