GOAt: Explaining Graph Neural Networks via Graph Output Attribution

Understanding the decision-making process of Graph Neural Networks (GNNs) is crucial to their interpretability. Most existing methods for explaining GNNs typically rely on training auxiliary models, resulting in the explanations remain black-boxed. This paper introduces Graph Output Attribution (GOA...

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Veröffentlicht in:	arXiv.org 2024-01
Hauptverfasser:	Lu, Shengyao, Mills, Keith G, Jiao He, Liu, Bang, Niu, Di
Format:	Artikel
Sprache:	eng
Schlagworte:	Graph neural networks Neural networks Nodes
Online-Zugang:	Volltext
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