Learning Long Range Spatio-Temporal Representations over Continuous Time Dynamic Graphs with State Space Models
2026-06-03 • Machine Learning
Machine LearningArtificial Intelligence
AI summaryⓘ
The authors developed a new method called CTDG-SSM to better understand how graphs change over time, especially when information needs to be remembered for a long time across the graph. Their approach uses a mathematical tool called CTT-HiPPO that combines time and graph structure memory, allowing the model to capture both local and global patterns. They translated this into a practical form that works efficiently in computers. Tested on tasks like predicting links and classifying nodes over time, their method showed better results, especially when long-term and long-distance information is important.
Continuous-time dynamic graphsState-space modelsTemporal dynamicsGraph LaplacianHiPPO operatorMemory-based modelsDynamic link predictionNode classificationLong-range information propagationPolynomial projection
Authors
Ayushman Raghuvanshi, Thummaluru Siddartha Readdy, Sundeep Prabhakar Chepuri, Mahesh Chandran
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 patterns. To mitigate this, we derive a parameter-efficient state-space modeling framework for continuous-time dynamic graphs (CTDG-SSM) from first principles. We first introduce continuous-time Topology-Aware higher order polynomial projection operator (CTT-HiPPO), a novel memory-based reformulation of HiPPO to jointly encode temporal dynamics and graph structure. The solution from CTT-HiPPO is obtained by projecting the classical HiPPO solution through a polynomial of the Laplacian matrix, yielding topology-aware memory updates that admit an equivalent state-space formulation for CTDGs (CTDG-SSM). Then a computationally efficient discrete formulation is obtained using the zero-order hold approach for model implementation. Across benchmarks on dynamic link prediction, dynamic node classification, and sequence classification, CTDG-SSM achieves state-of-the-art performance. Notably, it achieves large performance gains on datasets that require long range temporal (LRT) and spatial reasoning.