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20212026
most citedGCExplainer: Human-in-the-Loop Concept-based Explanations for Graph Neural Networks

14 citations · 36 across the 15 of their papers we have counts for

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cs.LG2026

Concept Graph Convolutions: Message Passing in the Concept Space

Lucie Charlotte Magister, Pietro Lio

The trust in the predictions of Graph Neural Networks is limited by their opaque reasoning process. Prior methods have tried to explain graph networks via concept-based explanation…

cs.LG2026

Subgraph Concept Networks: Concept Levels in Graph Classification

Lucie Charlotte Magister, Alexander Norcliffe, Iulia Duta +1

The reasoning process of Graph Neural Networks is complex and considered opaque, limiting trust in their predictions. To alleviate this issue, prior work has proposed concept-based…