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20232026
most citedTowards Robust Fidelity for Evaluating Explainability of Graph Neural Networks

4 citations · 6 across the 6 of their papers we have counts for

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2 papers · 1 filter

cs.LG2025★ 2 cited

Is Your Explanation Reliable: Confidence-Aware Explanation on Graph Neural Networks

Jiaxing Zhang, Xiaoou Liu, Dongsheng Luo +1

Explaining Graph Neural Networks (GNNs) has garnered significant attention due to the need for interpretability, enabling users to understand the behavior of these black-box models…

cs.LG2025

RISE: Radius of Influence based Subgraph Extraction for 3D Molecular Graph Explanation

Jingxiang Qu, Wenhan Gao, Jiaxing Zhang +4

3D Geometric Graph Neural Networks (GNNs) have emerged as transformative tools for modeling molecular data. Despite their predictive power, these models often suffer from limited i…