Learning Long Range Spatio-Temporal Representations over Continuous Time Dynamic Graphs with State Space Models

📰 ArXiv cs.AI

Learn to capture long-range spatio-temporal representations in continuous-time dynamic graphs using state space models, enabling better information propagation over time

advanced Published 4 Jun 2026

Action Steps

Apply state space models to continuous-time dynamic graphs to learn long-range spatio-temporal representations
Configure models to capture multi-hop or global structural patterns
Test the performance of the models on benchmark datasets
Compare the results with existing approaches to evaluate the effectiveness of the proposed method
Use the learned representations for downstream tasks such as prediction or clustering

Who Needs to Know This

Data scientists and AI researchers working with dynamic graph data can benefit from this approach to improve their models' ability to capture long-range temporal patterns, while software engineers can apply these techniques to develop more accurate predictive models

Key Insight

💡 State space models can effectively capture long-range information propagation in continuous-time dynamic graphs, outperforming existing approaches

Full Article

Title: Learning Long Range Spatio-Temporal Representations over Continuous Time Dynamic Graphs with State Space Models

Abstract:
arXiv:2606.04672v1 Announce Type: cross Abstract: Continuous-time dynamic graphs (CTDGs) provide a richer framework to capture fine-grained temporal patterns in evolving relational data. Long-range information propagation is a key challenge while learning representations, wherein it is important to retain and update information over long temporal horizons. Existing approaches restrict models to capture one-hop or local temporal neighborhoods and fail to capture multi-hop or global structural pat

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