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STX-Search: Explanation Search for Continuous Dynamic Spatio-Temporal Models

arXiv 2503.04509 ↗First posted 6 Mar 2025cs.LG cs.AIcited by 0

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Abstract (arXiv)

Recent improvements in the expressive power of spatio-temporal models have led to performance gains in many real-world applications, such as traffic forecasting and social network modelling. However, understanding the predictions from a model is crucial to ensure reliability and trustworthiness, particularly for high-risk applications, such as healthcare and transport. Few existing methods are able to generate explanations for models trained on continuous-time dynamic graph data and, of these, the computational complexity and lack of suitable explanation objectives pose challenges. In this paper, we propose Spatio-Temporal EXplanation Search (STX-Search), a novel method for generating instance-level explanations that is applicable to static and dynamic temporal graph structures. We introduce a novel search strategy and objective function, to find explanations that are highly faithful and interpretable. When compared with existing methods, STX-Search produces explanations of higher fidelity whilst optimising explanation size to maintain interpretability.

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References · 25

  1. 2017Graph Attention Networkscited by 8,923
  2. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,919
  3. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,576
  4. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,136
  5. 2016Deep Variational Information Bottleneckcited by 930
  6. 2019GNNExplainer: Generating Explanations for Graph Neural Networkscited by 737
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  12. 2020PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networkscited by 211
13 more
  1. 2022Evaluating Explainability for Graph Neural Networkscited by 87
  2. 2023Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunitiescited by 61
  3. 2022GStarX: Explaining Graph Neural Networks with Structure-Aware Cooperative Gamescited by 42
  4. 2024Long Range Propagation on Continuous-Time Dynamic Graphscited by 28
  5. 2023TempME: Towards the Explainability of Temporal Graph Neural Networks via Motif Discoverycited by 27
  6. 2023LibCity: A Unified Library Towards Efficient and Comprehensive Urban Spatial-Temporal Predictioncited by 20
  7. 2023Explainable Spatio-Temporal Graph Neural Networkscited by 13
  8. 2022An Explainer for Temporal Graph Neural Networkscited by 12
  9. 2024Self-Explainable Temporal Graph Networks based on Graph Information Bottleneckcited by 9
  10. 2023Efficient GNN Explanation via Learning Removal-based Attributioncited by 6
  11. 2020Mutual Information for Explainable Deep Learning of Multiscale Systemscited by 4
  12. 2022A case study of spatiotemporal forecasting techniques for weather forecastingcited by 3
  13. 2023STF: Spatial Temporal Fusion for Trajectory Predictioncited by 1

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