OrphicX: A Causality-Inspired Latent Variable Model for Interpreting Graph Neural Networks

29 March 2022

Wanyu Lin

Papers citing "OrphicX: A Causality-Inspired Latent Variable Model for Interpreting Graph Neural Networks"

34 / 34 papers shown

Title
i-WiViG: Interpretable Window Vision GNN Ivica Obadic D. Kangin Dario Augusto Borges Oliveira Plamen Angelov Xiao Xiang Zhu 54 0 0 11 Mar 2025
Energy-Based Concept Bottleneck Models: Unifying Prediction, Concept Intervention, and Probabilistic Interpretations Xin-Chao Xu Yi Qin Lu Mi Hao Wang X. Li 66 9 0 03 Jan 2025
PAGE: Parametric Generative Explainer for Graph Neural Network Yang Qiu Wei Liu Jun Wang Ruixuan Li BDL 23 0 0 26 Aug 2024
Towards Few-shot Self-explaining Graph Neural Networks Jingyu Peng Qi Liu Linan Yue Zaixi Zhang Kai Zhang Yunhao Sha MILM 19 2 0 14 Aug 2024
Graph Neural Network Causal Explanation via Neural Causal Models Arman Behnam Binghui Wang CML 40 3 0 12 Jul 2024
How Interpretable Are Interpretable Graph Neural Networks? Yongqiang Chen Yatao Bian Bo Han James Cheng 42 4 0 12 Jun 2024
Explainable Molecular Property Prediction: Aligning Chemical Concepts with Predictions via Language Models Zhenzhong Wang Zehui Lin Wanyu Lin Ming Yang Minggang Zeng Kay Chen Tan 21 3 0 25 May 2024
SynHING: Synthetic Heterogeneous Information Network Generation for Graph Learning and Explanation Ming-Yi Hong Yi-Hsiang Huang Shao-En Lin You-Chen Teng Chih-Yu Wang Che Lin 33 0 0 07 Jan 2024
Causal State Distillation for Explainable Reinforcement Learning Wenhao Lu Xufeng Zhao Thilo Fryen Jae Hee Lee Mengdi Li S. Magg Stefan Wermter CML 33 2 0 30 Dec 2023
When Graph Neural Network Meets Causality: Opportunities, Methodologies and An Outlook Wenzhao Jiang Hao Liu Hui Xiong CML AI4CE 24 2 0 19 Dec 2023
Predicting and Interpreting Energy Barriers of Metallic Glasses with Graph Neural Networks Haoyu Li Shichang Zhang Longwen Tang Mathieu Bauchy Yizhou Sun AI4CE 35 1 0 08 Dec 2023
Exploring Causal Learning through Graph Neural Networks: An In-depth Review Simi Job Xiaohui Tao Taotao Cai Haoran Xie Lin Li Jianming Yong Qing Li CML AI4CE 19 5 0 25 Nov 2023
Generative Explanations for Graph Neural Network: Methods and Evaluations Jialin Chen Kenza Amara Junchi Yu Rex Ying 37 3 0 09 Nov 2023
ACGAN-GNNExplainer: Auxiliary Conditional Generative Explainer for Graph Neural Networks Yiqiao Li Jianlong Zhou Yifei Dong Niusha Shafiabady Fang Chen LLMAG 11 4 0 29 Sep 2023
Structure-Sensitive Graph Dictionary Embedding for Graph Classification Guangyi Liu Tong Zhang Xudong Wang Wenting Zhao Chuanwei Zhou Zhen Cui 25 1 0 18 Jun 2023
Self-Interpretable Time Series Prediction with Counterfactual Explanations Jingquan Yan Hao Wang BDL AI4TS 8 13 0 09 Jun 2023
Efficient GNN Explanation via Learning Removal-based Attribution Yao Rong Guanchu Wang Qizhang Feng Ninghao Liu Zirui Liu Enkelejda Kasneci Xia Hu 15 9 0 09 Jun 2023
A Survey on Explainability of Graph Neural Networks Jaykumar Kakkad Jaspal Jannu Kartik Sharma Charu C. Aggarwal Sourav Medya 28 23 0 02 Jun 2023
Unstructured and structured data: Can we have the best of both worlds with large language models? W. Tan 11 1 0 25 Apr 2023
Dynamic Graph Representation Learning with Neural Networks: A Survey Leshanshui Yang Sébastien Adam Clément Chatelain AI4TS AI4CE 26 14 0 12 Apr 2023
Distill n' Explain: explaining graph neural networks using simple surrogates Tamara A. Pereira Erik Nasciment Lucas Resck Diego Mesquita Amauri Souza 22 15 0 17 Mar 2023
Causal Temporal Graph Convolutional Neural Networks (CTGCN) Abigail Langbridge Fearghal O'Donncha Amadou Ba Fabio Lorenzi Christopher Lohse J. Ploennigs GNN 15 1 0 16 Mar 2023
PaGE-Link: Path-based Graph Neural Network Explanation for Heterogeneous Link Prediction Shichang Zhang Jiani Zhang Xiang Song Soji Adeshina Da Zheng Christos Faloutsos Yizhou Sun LRM 17 31 0 24 Feb 2023
CI-GNN: A Granger Causality-Inspired Graph Neural Network for Interpretable Brain Network-Based Psychiatric Diagnosis Kaizhong Zheng Shujian Yu Badong Chen CML 21 31 0 04 Jan 2023
Explaining the Explainers in Graph Neural Networks: a Comparative Study Antonio Longa Steve Azzolin G. Santin G. Cencetti Pietro Lio' Bruno Lepri Andrea Passerini 46 27 0 27 Oct 2022
Global Explainability of GNNs via Logic Combination of Learned Concepts Steve Azzolin Antonio Longa Pietro Barbiero Pietro Lio' Andrea Passerini 25 54 0 13 Oct 2022
L2XGNN: Learning to Explain Graph Neural Networks G. Serra Mathias Niepert 26 7 0 28 Sep 2022
GNNInterpreter: A Probabilistic Generative Model-Level Explanation for Graph Neural Networks Xiaoqi Wang Hang Shen 22 41 0 15 Sep 2022
Trustworthy Graph Neural Networks: Aspects, Methods and Trends He Zhang Bang Wu Xingliang Yuan Shirui Pan Hanghang Tong Jian Pei 41 104 0 16 May 2022
Causal Transportability for Visual Recognition Chengzhi Mao K. Xia James Wang Hongya Wang Junfeng Yang Elias Bareinboim Carl Vondrick CML OOD BDL 15 35 0 26 Apr 2022
Task-Agnostic Graph Explanations Yaochen Xie S. Katariya Xianfeng Tang E-Wen Huang Nikhil S. Rao Karthik Subbian Shuiwang Ji 21 25 0 16 Feb 2022
GStarX: Explaining Graph Neural Networks with Structure-Aware Cooperative Games Shichang Zhang Yozen Liu Neil Shah Yizhou Sun FAtt 23 45 0 28 Jan 2022
Instrumental Variable-Driven Domain Generalization with Unobserved Confounders Junkun Yuan Xu Ma Ruoxuan Xiong Mingming Gong Xiangyu Liu Fei Wu Lanfen Lin Kun Kuang OOD CML 18 11 0 04 Oct 2021
Geometric deep learning on graphs and manifolds using mixture model CNNs Federico Monti Davide Boscaini Jonathan Masci Emanuele Rodolà Jan Svoboda M. Bronstein GNN 234 1,811 0 25 Nov 2016