When Graph Neural Network Meets Causality: Opportunities, Methodologies
and An Outlook

When Graph Neural Network Meets Causality: Opportunities, Methodologies and An Outlook

19 December 2023

Papers citing "When Graph Neural Network Meets Causality: Opportunities, Methodologies and An Outlook"

19 / 19 papers shown

Title
Addressing Shortcomings in Fair Graph Learning Datasets: Towards a New Benchmark Xiaowei Qian Zhimeng Guo Jialiang Li Haitao Mao Bingheng Li Suhang Wang Yao Ma 21 2 0 09 Mar 2024
Learning Invariant Molecular Representation in Latent Discrete Space Zhuang Xiang Qiang Zhang Keyan Ding Yatao Bian Xiao Wang Jingsong Lv Hongyang Chen Huajun Chen OOD 18 8 0 22 Oct 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 30 10 0 14 Oct 2023
Towards out-of-distribution generalizable predictions of chemical kinetics properties Zihao W. Wang Yongqiang Chen Yang Duan Weijiang Li Bo Han James Cheng Hanghang Tong OOD 21 6 0 04 Oct 2023
Mind the Label Shift of Augmentation-based Graph OOD Generalization Junchi Yu Jian Liang R. He 29 19 0 27 Mar 2023
A Survey on Graph Counterfactual Explanations: Definitions, Methods, Evaluation, and Research Challenges Mario Alfonso Prado-Romero Bardh Prenkaj Giovanni Stilo F. Giannotti CML 19 25 0 21 Oct 2022
Practical Adversarial Attacks on Spatiotemporal Traffic Forecasting Models F. Liu Haowen Liu Wenzhao Jiang OOD 58 22 0 05 Oct 2022
Feature-based Learning for Diverse and Privacy-Preserving Counterfactual Explanations Vy Vo Trung Le Van Nguyen He Zhao Edwin V. Bonilla Gholamreza Haffari Dinh Q. Phung CML 24 13 0 27 Sep 2022
How Powerful are Spectral Graph Neural Networks Xiyuan Wang Muhan Zhang 70 175 0 23 May 2022
Trustworthy Graph Neural Networks: Aspects, Methods and Trends He Zhang Bang Wu Xingliang Yuan Shirui Pan Hanghang Tong Jian Pei 34 98 0 16 May 2022
Discovering Invariant Rationales for Graph Neural Networks Yingmin Wu Xiang Wang An Zhang Xiangnan He Tat-Seng Chua OOD AI4CE 87 170 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 46 57 0 24 Jan 2022
MugRep: A Multi-Task Hierarchical Graph Representation Learning Framework for Real Estate Appraisal Weijia Zhang Hao Liu Lijun Zha Hengshu Zhu Ji Liu Dejing Dou Hui Xiong 29 21 0 12 Jul 2021
Causal Learning for Socially Responsible AI Lu Cheng Ahmadreza Mosallanezhad Paras Sheth Huan Liu 58 10 0 25 Apr 2021
CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks Ana Lucic Maartje ter Hoeve Gabriele Tolomei Maarten de Rijke Fabrizio Silvestri 97 87 0 05 Feb 2021
Explainability in Graph Neural Networks: A Taxonomic Survey Hao Yuan Haiyang Yu Shurui Gui Shuiwang Ji 156 463 0 31 Dec 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 212 888 0 02 Mar 2020
Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation Jiaxuan You Bowen Liu Rex Ying Vijay S. Pande J. Leskovec GNN 181 878 0 07 Jun 2018
Learning Representations for Counterfactual Inference Fredrik D. Johansson Uri Shalit David Sontag CML OOD BDL 205 713 0 12 May 2016