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arXiv · 2405.06917

Design Requirements for Human-Centered Graph Neural Network Explanations

Pantea Habibi·Peyman Baghershahi·Sourav Medya·Debaleena Chattopadhyay
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Abstract

Graph neural networks (GNNs) are powerful graph-based machine-learning models that are popular in various domains, e.g., social media, transportation, and drug discovery. However, owing to complex data representations, GNNs do not easily allow for human-intelligible explanations of their predictions, which can decrease trust in them as well as deter any collaboration opportunities between the AI expert and non-technical, domain expert. Here, we first discuss the two papers that aim to provide GNN explanations to domain experts in an accessible manner and then establish a set of design requirements for human-centered GNN explanations. Finally, we offer two example prototypes to demonstrate some of those proposed requirements.

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Pantea Habibi, Peyman Baghershahi, Sourav Medya, Debaleena Chattopadhyay. 2024-05-11. Design Requirements for Human-Centered Graph Neural Network Explanations. https://arxiv.org/abs/2405.06917

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