mlsanityData to 14 Oct 2026

Design Requirements for Human-Centered Graph Neural Network Explanations

arXiv 2405.06917 ↗First posted 11 May 2024cs.LG cs.HCcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 16 of its 30 references that are in the map. First posted on arXiv 11 May 2024.

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16 of 30 references.

References · 16

  1. 2017Graph Attention Networkscited by 8,923
  2. 2017Explanation in Artificial Intelligence: Insights from the Social Sciencescited by 1,206
  3. 2018Graph Convolutional Neural Networks for Web-Scale Recommender Systemscited by 1,138
  4. 2019GNNExplainer: Generating Explanations for Graph Neural Networkscited by 737
  5. 2019EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphscited by 447
  6. 2020Questioning the AI: Informing Design Practices for Explainable AI User Experiencescited by 253
  7. 2020Interpreting Graph Neural Networks for NLP With Differentiable Edge Maskingcited by 153
  8. 2021Human-Centered Explainable AI (XAI): From Algorithms to User Experiencescited by 123
  9. 2021CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networkscited by 119
  10. 2020Human-centered Explainable AI: Towards a Reflective Sociotechnical Approachcited by 90
  11. 2022Global Explainability of GNNs via Logic Combination of Learned Conceptscited by 65
  12. 2021GREED: A Neural Framework for Learning Graph Distance Functionscited by 32
4 more
  1. 2022Towards Involving End-users in Interactive Human-in-the-loop AI Fairnesscited by 12
  2. 2021Visualizing Graph Neural Networks with CorGIE: Corresponding a Graph to Its Embeddingcited by 8
  3. 2023A Co-design Study for Multi-Stakeholder Job Recommender System Explanationscited by 4
  4. 2023RekomGNN: Visualizing, Contextualizing and Evaluating Graph Neural Networks Recommendationscited by 1

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