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October 8, 2025Open Access

Beyond Attention: Learning Spatio-Temporal Dynamics with Emergent Interpretable Topologies

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Authors

SASai Vamsi AlisettiVKVikas KalagiSKSanjukta KrishnagopalUniversity of California, Santa Barbara

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Overview

InterGAT demonstrates improved predictive accuracy by learning node interactions in spatio-temporal data, suggesting new avenues for interpretability.

Key Points

  • InterGAT achieves at least a 21% improvement in forecasting accuracy over the baseline GAT-GRU.
  • The model significantly reduces training time by 60-70% compared to the GAT-GRU baseline.
  • Using a fully learnable interaction matrix enables the capture of interpretable, topology-aware attention patterns.
  • Spectral and clustering analyses reveal the model effectively captures both localized and global dynamics.

Cite This Study

Alisetti et al. (2025) studied this question.

synapsesocial.com/papers/68e6f342f8145af55aeacc18https://doi.org/10.48550/arxiv.2506.00770
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