What question did this study set out to answer?

This research investigates the challenges in explaining Graph Neural Networks, particularly regarding distribution shifts between training and explanation subgraphs.

May 7, 2026IEEE Transactions on Pattern Analysis and Machine Intelligence0 citations

Addressing Distribution Shift in Graph Neural Network Explanations

Addressing Structural Distribution Shift in Explanations for Graph Neural Networks

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Authors

ZCZhuomin Chen HSHojat Allah Salehi ESEsteban Schafir

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Overview

Empirical studies reveal improved quality and reliability of GNN explanations, suggesting better trust in decision-making.

Key Points

This research investigates the challenges in explaining Graph Neural Networks, particularly regarding distribution shifts between training and explanation subgraphs.
Developed a theoretical framework formalizing explanation subgraphs through sufficiency and minimality criteria.
Conducted theoretical analysis and empirical studies on diverse datasets.
Optimized explanatory information retention through parametric and non-parametric approaches.
Identified a fundamental distributional disparity between explanation subgraphs and original graphs.
Proposed the concept of proxy graphs to maintain essential explanatory information while conforming to original data distribution.
Showed improvements in the quality and reliability of GNN explanations through empirical evaluations.

Cite This Study

Chen et al. (2026) studied this question.

synapsesocial.com/papers/69fbe357164b5133a91a2a40 https://doi.org/10.1109/tpami.2026.3690304

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May 7, 2026IEEE Transactions on Pattern Analysis and Machine Intelligence0 citations

Addressing Distribution Shift in Graph Neural Network Explanations

Addressing Structural Distribution Shift in Explanations for Graph Neural Networks

View Full Paper

Ask AI

Bookmark

Authors

ZCZhuomin Chen HSHojat Allah Salehi ESEsteban Schafir

Discussion

Member takes

Overview

Empirical studies reveal improved quality and reliability of GNN explanations, suggesting better trust in decision-making.

Key Points

This research investigates the challenges in explaining Graph Neural Networks, particularly regarding distribution shifts between training and explanation subgraphs.
Developed a theoretical framework formalizing explanation subgraphs through sufficiency and minimality criteria.
Conducted theoretical analysis and empirical studies on diverse datasets.
Optimized explanatory information retention through parametric and non-parametric approaches.
Identified a fundamental distributional disparity between explanation subgraphs and original graphs.
Proposed the concept of proxy graphs to maintain essential explanatory information while conforming to original data distribution.
Showed improvements in the quality and reliability of GNN explanations through empirical evaluations.

Cite This Study

Chen et al. (2026) studied this question.

synapsesocial.com/papers/69fbe357164b5133a91a2a40 https://doi.org/10.1109/tpami.2026.3690304

View Full Paper

Ask AI

Bookmark