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Robust Counterfactual Explanations on Graph Neural Networks

Robust Counterfactual Explanations on Graph Neural Networks

8 July 2021
Mohit Bajaj
Lingyang Chu
Zihui Xue
J. Pei
Lanjun Wang
P. C. Lam
Yong Zhang
    OOD
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Papers citing "Robust Counterfactual Explanations on Graph Neural Networks"

20 / 20 papers shown
Title
COMRECGC: Global Graph Counterfactual Explainer through Common Recourse
COMRECGC: Global Graph Counterfactual Explainer through Common Recourse
Gregoire Fournier
Sourav Medya
BDL
46
0
0
11 May 2025
Interpretability of Graph Neural Networks to Assess Effects of Global Change Drivers on Ecological Networks
Interpretability of Graph Neural Networks to Assess Effects of Global Change Drivers on Ecological Networks
Emré Anakok
Pierre Barbillon
Colin Fontaine
Elisa Thébault
49
0
0
19 Mar 2025
Recent Advances in Malware Detection: Graph Learning and Explainability
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
76
2
0
14 Feb 2025
F-Fidelity: A Robust Framework for Faithfulness Evaluation of Explainable AI
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
38
0
0
03 Oct 2024
Explainable Graph Neural Networks Under Fire
Explainable Graph Neural Networks Under Fire
Zhong Li
Simon Geisler
Yuhang Wang
Stephan Günnemann
M. Leeuwen
AAML
43
0
0
10 Jun 2024
A Survey of Graph Neural Networks in Real world: Imbalance, Noise,
  Privacy and OOD Challenges
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
36
35
0
07 Mar 2024
When Graph Neural Network Meets Causality: Opportunities, Methodologies
  and An Outlook
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
Robust Stochastic Graph Generator for Counterfactual Explanations
Robust Stochastic Graph Generator for Counterfactual Explanations
Mario Alfonso Prado-Romero
Bardh Prenkaj
Giovanni Stilo
CML
13
3
0
18 Dec 2023
D4Explainer: In-Distribution GNN Explanations via Discrete Denoising
  Diffusion
D4Explainer: In-Distribution GNN Explanations via Discrete Denoising Diffusion
Jialin Chen
Shirley Wu
Abhijit Gupta
Rex Ying
DiffM
42
4
0
30 Oct 2023
Robust Ante-hoc Graph Explainer using Bilevel Optimization
Robust Ante-hoc Graph Explainer using Bilevel Optimization
Kha-Dinh Luong
Mert Kosan
A. Silva
Ambuj K. Singh
34
6
0
25 May 2023
GANExplainer: GAN-based Graph Neural Networks Explainer
GANExplainer: GAN-based Graph Neural Networks Explainer
Yiqiao Li
Jianlong Zhou
Boyuan Zheng
Fang Chen
LLMAG
37
4
0
30 Dec 2022
Global Counterfactual Explainer for Graph Neural Networks
Global Counterfactual Explainer for Graph Neural Networks
Mert Kosan
Zexi Huang
Sourav Medya
Sayan Ranu
Ambuj K. Singh
31
47
0
21 Oct 2022
CLEAR: Generative Counterfactual Explanations on Graphs
CLEAR: Generative Counterfactual Explanations on Graphs
Jing Ma
Ruocheng Guo
Saumitra Mishra
Aidong Zhang
Jundong Li
CML
OOD
35
53
0
16 Oct 2022
A Survey of Explainable Graph Neural Networks: Taxonomy and Evaluation
  Metrics
A Survey of Explainable Graph Neural Networks: Taxonomy and Evaluation Metrics
Yiqiao Li
Jianlong Zhou
Sunny Verma
Fang Chen
XAI
34
39
0
26 Jul 2022
A Survey of Trustworthy Graph Learning: Reliability, Explainability, and
  Privacy Protection
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
37
25
0
20 May 2022
Cardinality-Minimal Explanations for Monotonic Neural Networks
Cardinality-Minimal Explanations for Monotonic Neural Networks
Ouns El Harzli
Bernardo Cuenca Grau
Ian Horrocks
FAtt
38
5
0
19 May 2022
Trustworthy Graph Neural Networks: Aspects, Methods and Trends
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
A Framework and Benchmarking Study for Counterfactual Generating Methods
  on Tabular Data
A Framework and Benchmarking Study for Counterfactual Generating Methods on Tabular Data
Raphael Mazzine
David Martens
32
33
0
09 Jul 2021
Zorro: Valid, Sparse, and Stable Explanations in Graph Neural Networks
Zorro: Valid, Sparse, and Stable Explanations in Graph Neural Networks
Thorben Funke
Megha Khosla
Mandeep Rathee
Avishek Anand
FAtt
23
38
0
18 May 2021
NCGNN: Node-Level Capsule Graph Neural Network for Semisupervised
  Classification
NCGNN: Node-Level Capsule Graph Neural Network for Semisupervised Classification
Rui Yang
Wenrui Dai
Chenglin Li
Junni Zou
H. Xiong
28
20
0
07 Dec 2020
1