PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networks

12 October 2020

Papers citing "PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networks"

50 / 58 papers shown

Title
COMRECGC: Global Graph Counterfactual Explainer through Common Recourse Gregoire Fournier Sourav Medya BDL 44 0 0 11 May 2025
Framework GNN-AID: Graph Neural Network Analysis Interpretation and Defense Kirill Lukyanov Mikhail Drobyshevskiy Georgii Sazonov Mikhail Soloviov Ilya Makarov GNN 46 0 0 06 May 2025
Interpretability of Graph Neural Networks to Assess Effects of Global Change Drivers on Ecological Networks Emré Anakok Pierre Barbillon Colin Fontaine Elisa Thébault 47 0 0 19 Mar 2025
Recent Advances in Malware Detection: Graph Learning and Explainability Hossein Shokouhinejad Roozbeh Razavi-Far Hesamodin Mohammadian Mahdi Rabbani Samuel Ansong Griffin Higgins Ali Ghorbani AAML 73 2 0 14 Feb 2025
On the Probability of Necessity and Sufficiency of Explaining Graph Neural Networks: A Lower Bound Optimization Approach Ruichu Cai Yuxuan Zhu Xuexin Chen Yuan Fang Min-man Wu Jie Qiao Z. Hao 51 7 0 31 Dec 2024
F-Fidelity: A Robust Framework for Faithfulness Evaluation of Explainable AI Xu Zheng Farhad Shirani Zhuomin Chen Chaohao Lin Wei Cheng Wenbo Guo Dongsheng Luo AAML 28 0 0 03 Oct 2024
On GNN explanability with activation rules Luca Veyrin-Forrer Ataollah Kamal Stefan Duffner Marc Plantevit C. Robardet AI4CE 23 2 0 17 Jun 2024
Explainable Graph Neural Networks Under Fire Zhong Li Simon Geisler Yuhang Wang Stephan Günnemann M. Leeuwen AAML 43 0 0 10 Jun 2024
From Latent to Lucid: Transforming Knowledge Graph Embeddings into Interpretable Structures with KGEPrisma Christoph Wehner Chrysa Iliopoulou Ute Schmid Tarek R. Besold 60 0 0 03 Jun 2024
MAGE: Model-Level Graph Neural Networks Explanations via Motif-based Graph Generation Zhaoning Yu Hongyang Gao 47 3 0 21 May 2024
A Differential Geometric View and Explainability of GNN on Evolving Graphs Yazheng Liu Xi Zhang Sihong Xie 19 3 0 11 Mar 2024
Incorporating Retrieval-based Causal Learning with Information Bottlenecks for Interpretable Graph Neural Networks Jiahua Rao Jiancong Xie Hanjing Lin Shuangjia Zheng Zhen Wang Yuedong Yang 21 0 0 07 Feb 2024
Towards Fine-Grained Explainability for Heterogeneous Graph Neural Network Tong Li Jiale Deng Yanyan Shen Luyu Qiu Hu Yongxiang Caleb Chen Cao 23 5 0 23 Dec 2023
Generating Explanations to Understand and Repair Embedding-based Entity Alignment Xiaobin Tian Zequn Sun Wei Hu 26 6 0 08 Dec 2023
D4Explainer: In-Distribution GNN Explanations via Discrete Denoising Diffusion Jialin Chen Shirley Wu Abhijit Gupta Rex Ying DiffM 39 4 0 30 Oct 2023
DEGREE: Decomposition Based Explanation For Graph Neural Networks Qizhang Feng Ninghao Liu Fan Yang Ruixiang Tang Mengnan Du Xia Hu 23 22 0 22 May 2023
Unstructured and structured data: Can we have the best of both worlds with large language models? W. Tan 18 1 0 25 Apr 2023
Cultural-aware Machine Learning based Analysis of COVID-19 Vaccine Hesitancy Raed Alharbi Sylvia M. Chan-Olmsted Huan-huan Chen My T. Thai 8 2 0 14 Apr 2023
Illuminati: Towards Explaining Graph Neural Networks for Cybersecurity Analysis Haoyu He Yuede Ji H. H. Huang 25 20 0 26 Mar 2023
Structural Explanations for Graph Neural Networks using HSIC Ayato Toyokuni Makoto Yamada 24 1 0 04 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 25 31 0 04 Jan 2023
On the Limit of Explaining Black-box Temporal Graph Neural Networks Minh Nhat Vu My T. Thai 16 4 0 02 Dec 2022
xEM: Explainable Entity Matching in Customer 360 Sukriti Jaitly Deepa Mariam George Balaji Ganesan Muhammad Ameen Srinivas Pusapati 16 0 0 01 Dec 2022
Global Counterfactual Explainer for Graph Neural Networks Mert Kosan Zexi Huang Sourav Medya Sayan Ranu Ambuj K. Singh 26 47 0 21 Oct 2022
L2XGNN: Learning to Explain Graph Neural Networks G. Serra Mathias Niepert 33 7 0 28 Sep 2022
Defending Against Backdoor Attack on Graph Nerual Network by Explainability B. Jiang Zhao Li AAML GNN 56 16 0 07 Sep 2022
An Explainer for Temporal Graph Neural Networks Wenchong He Minh Nhat Vu Zhe Jiang My T. Thai AI4TS 9 17 0 02 Sep 2022
Global Concept-Based Interpretability for Graph Neural Networks via Neuron Analysis Xuanyuan Han Pietro Barbiero Dobrik Georgiev Lucie Charlotte Magister Pietro Lió MILM 34 41 0 22 Aug 2022
Evaluating Explainability for Graph Neural Networks Chirag Agarwal Owen Queen Himabindu Lakkaraju Marinka Zitnik 45 99 0 19 Aug 2022
A Survey of Explainable Graph Neural Networks: Taxonomy and Evaluation Metrics Yiqiao Li Jianlong Zhou Sunny Verma Fang Chen XAI 31 39 0 26 Jul 2022
ViGAT: Bottom-up event recognition and explanation in video using factorized graph attention network Nikolaos Gkalelis Dimitrios Daskalakis Vasileios Mezaris 13 10 0 20 Jul 2022
Interpretable Graph Neural Networks for Connectome-Based Brain Disorder Analysis Hejie Cui Wei Dai Yanqiao Zhu Xiaoxiao Li Lifang He Carl Yang 71 78 0 30 Jun 2022
BAGEL: A Benchmark for Assessing Graph Neural Network Explanations Mandeep Rathee Thorben Funke Avishek Anand Megha Khosla 38 14 0 28 Jun 2022
Towards Faithful and Consistent Explanations for Graph Neural Networks Tianxiang Zhao Dongsheng Luo Xiang Zhang Suhang Wang FAtt 52 18 0 27 May 2022
Faithful Explanations for Deep Graph Models Zifan Wang Yuhang Yao Chaoran Zhang Han Zhang Youjie Kang Carlee Joe-Wong Matt Fredrikson Anupam Datta FAtt 19 2 0 24 May 2022
A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection Bingzhe Wu Jintang Li Junchi Yu Yatao Bian Hengtong Zhang ... Guangyu Sun Peng Cui Zibin Zheng Zhe Liu P. Zhao OOD 30 25 0 20 May 2022
Trustworthy Graph Neural Networks: Aspects, Methods and Trends He Zhang Bang Wu Xingliang Yuan Shirui Pan Hanghang Tong Jian Pei 45 104 0 16 May 2022
Finding Global Homophily in Graph Neural Networks When Meeting Heterophily Xiang Li Renyu Zhu Yao Cheng Caihua Shan Siqiang Luo Dongsheng Li Wei Qian 15 181 0 15 May 2022
OrphicX: A Causality-Inspired Latent Variable Model for Interpreting Graph Neural Networks Wanyu Lin Hao Lan Hao Wang Baochun Li BDL CML 33 50 0 29 Mar 2022
Explainability in Graph Neural Networks: An Experimental Survey Peibo Li Yixing Yang M. Pagnucco Yang Song 23 31 0 17 Mar 2022
Interpretable and Generalizable Graph Learning via Stochastic Attention Mechanism Siqi Miao Miaoyuan Liu Pan Li 14 197 0 31 Jan 2022
Deconfounding to Explanation Evaluation in Graph Neural Networks Yingmin Wu Xiang Wang An Zhang Xia Hu Fuli Feng Xiangnan He Tat-Seng Chua FAtt CML 17 14 0 21 Jan 2022
Self-learn to Explain Siamese Networks Robustly Chao Chen Yifan Shen Guixiang Ma Xiangnan Kong S. Rangarajan Xi Zhang Sihong Xie 43 5 0 15 Sep 2021
Jointly Attacking Graph Neural Network and its Explanations Wenqi Fan Wei Jin Xiaorui Liu Han Xu Xianfeng Tang Suhang Wang Qing Li Jiliang Tang Jianping Wang Charu C. Aggarwal AAML 42 28 0 07 Aug 2021
GCExplainer: Human-in-the-Loop Concept-based Explanations for Graph Neural Networks Lucie Charlotte Magister Dmitry Kazhdan Vikash Singh Pietro Lió 27 48 0 25 Jul 2021
Bridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural Networks Zhiqian Chen Fanglan Chen Lei Zhang Taoran Ji Kaiqun Fu Liang Zhao Feng Chen Lingfei Wu Charu C. Aggarwal Chang-Tien Lu 38 18 0 21 Jul 2021
BrainNNExplainer: An Interpretable Graph Neural Network Framework for Brain Network based Disease Analysis Hejie Cui Wei Dai Yanqiao Zhu Xiaoxiao Li Lifang He Carl Yang 18 27 0 11 Jul 2021
Robust Counterfactual Explanations on Graph Neural Networks Mohit Bajaj Lingyang Chu Zihui Xue J. Pei Lanjun Wang P. C. Lam Yong Zhang OOD 40 96 0 08 Jul 2021
A Survey on Graph-Based Deep Learning for Computational Histopathology David Ahmedt-Aristizabal M. Armin Simon Denman Clinton Fookes L. Petersson GNN AI4CE 19 107 0 01 Jul 2021
Reimagining GNN Explanations with ideas from Tabular Data Anjali Singh K. ShamanthRNayak Balaji Ganesan LMTD 20 1 0 23 Jun 2021