STX-Search: Explanation Search for Continuous Dynamic Spatio-Temporal Models
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866910861583450112 |
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| author | Anwar, Saif Griffiths, Nathan Popham, Thomas Bhalerao, Abhir |
| author_facet | Anwar, Saif Griffiths, Nathan Popham, Thomas Bhalerao, Abhir |
| contents | 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 $\textbf{S}$patio-$\textbf{T}$emporal E$\textbf{X}$planation $\textbf{Search}$ (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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2503_04509 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | STX-Search: Explanation Search for Continuous Dynamic Spatio-Temporal Models Anwar, Saif Griffiths, Nathan Popham, Thomas Bhalerao, Abhir Machine Learning Artificial Intelligence 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 $\textbf{S}$patio-$\textbf{T}$emporal E$\textbf{X}$planation $\textbf{Search}$ (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. |
| title | STX-Search: Explanation Search for Continuous Dynamic Spatio-Temporal Models |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2503.04509 |