CTDG-SSM introduces CTT-HiPPO, a Laplacian-polynomial projection of HiPPO, to create a parameter-efficient state-space formulation for continuous-time dynamic graphs that captures long-range spatio-temporal patterns.
Dygmamba: Efficiently modeling long-term temporal dependency on continuous-time dynamic graphs with state space models
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DuConTE is a dual-granularity text encoder that incorporates graph topology into language model attention for improved node representations in text-attributed graphs.
A survey tracing the evolution of state-space models like S4 and Mamba, their efficiency trade-offs, and applications in NLP, vision, and other domains.
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DuConTE: Dual-Granularity Text Encoder with Topology-Constrained Attention for Text-attributed Graphs
DuConTE is a dual-granularity text encoder that incorporates graph topology into language model attention for improved node representations in text-attributed graphs.