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On the Probability of Necessity and Sufficiency of Explaining Graph Neural Networks: A Lower Bound Optimization Approach

On the Probability of Necessity and Sufficiency of Explaining Graph Neural Networks: A Lower Bound Optimization Approach

31 December 2024
Ruichu Cai
Yuxuan Zhu
Xuexin Chen
Yuan Fang
Min-man Wu
Jie Qiao
Z. Hao
ArXivPDFHTML

Papers citing "On the Probability of Necessity and Sufficiency of Explaining Graph Neural Networks: A Lower Bound Optimization Approach"

3 / 3 papers shown
Title
Unifying Invariant and Variant Features for Graph Out-of-Distribution
  via Probability of Necessity and Sufficiency
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
36
0
0
21 Jul 2024
Explainability in Graph Neural Networks: A Taxonomic Survey
Explainability in Graph Neural Networks: A Taxonomic Survey
Hao Yuan
Haiyang Yu
Shurui Gui
Shuiwang Ji
159
589
0
31 Dec 2020
A causal framework for explaining the predictions of black-box
  sequence-to-sequence models
A causal framework for explaining the predictions of black-box sequence-to-sequence models
David Alvarez-Melis
Tommi Jaakkola
CML
210
201
0
06 Jul 2017
1