Encoding Concepts in Graph Neural Networks

• URL: http://arxiv.org/abs/2207.13586v1
• Date: Wed, 27 Jul 2022 15:34:14 GMT
• Title: Encoding Concepts in Graph Neural Networks
• Authors: Lucie Charlotte Magister and Pietro Barbiero and Dmitry Kazhdan and Federico Siciliano and Gabriele Ciravegna and Fabrizio Silvestri and Pietro Lio and Mateja Jamnik
• Abstract summary: We introduce the Concept Module, the first differentiable concept-discovery approach for graph networks. The proposed approach makes graph networks explainable by design by first discovering graph concepts and then using these to solve the task. Our results demonstrate that this approach allows graph networks to attain model accuracy comparable with their equivalent vanilla versions.
• Score: 6.129235861306906
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The opaque reasoning of Graph Neural Networks induces a lack of human trust. Existing graph network explainers attempt to address this issue by providing post-hoc explanations, however, they fail to make the model itself more interpretable. To fill this gap, we introduce the Concept Encoder Module, the first differentiable concept-discovery approach for graph networks. The proposed approach makes graph networks explainable by design by first discovering graph concepts and then using these to solve the task. Our results demonstrate that this approach allows graph networks to: (i) attain model accuracy comparable with their equivalent vanilla versions, (ii) discover meaningful concepts that achieve high concept completeness and purity scores, (iii) provide high-quality concept-based logic explanations for their prediction, and (iv) support effective interventions at test time: these can increase human trust as well as significantly improve model performance.

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