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Forget Less, Generalize More: Unifying Temporal and Structural Adaptation for Dynamic Graphs

arXiv — cs.LG•Friday, May 29, 2026 at 4:00:00 AM
PositiveArtificial Intelligence
  • What Happened

    A new framework called Dual-Scale Retentive Dynamics (DSRD) has been proposed for representation learning on dynamic graphs, addressing the limitations of existing methods that rely on fixed temporal decay and predetermined structural propagation depths. DSRD integrates a retentive state that captures both temporal memory and structural context, enhancing the ability to model complex dependencies in evolving graphs.

  • Why It Matters

    This development is significant as it allows for improved generalization across diverse graphs with varying interaction frequencies and topological characteristics, potentially advancing the field of artificial intelligence and graph analysis.

— via World Pulse Now AI Editorial System
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