Publication Type
Journal Article
Version
submittedVersion
Publication Date
4-2025
Abstract
The explainability of Graph Neural Networks (GNNs) is critical to various GNN applications, yet it remains a significant challenge. A convincing explanation should be both necessary and sufficient simultaneously. However, existing GNN explaining approaches focus on only one of the two aspects, necessity or sufficiency, or a heuristic trade-off between the two. Theoretically, the Probability of Necessity and Sufficiency (PNS) holds the potential to identify the most necessary and sufficient explanation since it can mathematically quantify the necessity and sufficiency of an explanation. Nevertheless, the difficulty of obtaining PNS due to non-monotonicity and the challenge of counterfactual estimation limit its wide use. To address the non-identifiability of PNS, we resort to a lower bound of PNS that can be optimized via counterfactual estimation, and propose a framework of Necessary and Sufficient Explanation for GNN (NSEG) via optimizing that lower bound. Specifically, we depict the GNN as a structural causal model (SCM), and estimate the probability of counterfactual via the intervention under the SCM. Additionally, we leverage continuous masks with a sampling strategy to optimize the lower bound to enhance the scalability. Empirical results demonstrate that NSEG outperforms state-of-the-art methods, consistently generating the most necessary and sufficient explanations. The implementation of our NSEG is available at https://github.com/EthanChu7/NSEG.
Keywords
Causality, Explainability, Explainable AI, Graph Neural Networks, Interpretability
Discipline
Numerical Analysis and Scientific Computing | OS and Networks
Publication
Neural Networks
Volume
184
First Page
1
Last Page
17
ISSN
0893-6080
Identifier
10.1016/j.neunet.2024.107065
Publisher
Elsevier
Citation
CAI, Ruichu; ZHU, Yuxuan; CHEN, Xuexin; FANG, Yuan; WU, Min; QIAO, Jie; and HAO, Zhifeng.
On the probability of necessity and sufficiency of explaining Graph Neural Networks: A lower bound optimization approach. (2025). Neural Networks. 184, 1-17.
Available at: https://ink.library.smu.edu.sg/sis_research/10111
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1016/j.neunet.2024.107065
