Forget Less, Generalize More: Unifying Temporal and Structural Adaptation for Dynamic Graphs
arXiv:2605.29453v1 Announce Type: new Abstract: Representation learning on dynamic graphs requires capturing complex dependencies that evolve across both time and structure. Existing approaches typically adopt fixed temporal decay schemes or predetermined structural propagation depths, limiting their ability to generalize across graphs with diverse interaction frequencies and topological characteristics. We propose Dual-Scale Retentive Dynamics (DSRD), a unified framework that maintains a retentive representation state encoding both temporal memory and structural context. DSRD introduces two k
The increasing complexity and scale of dynamic graph data in AI applications necessitate more sophisticated representation learning models that can generalize effectively across diverse temporal and structural patterns.
This research introduces a unified framework that improves the ability of AI models to understand and predict evolving relationships, crucial for advancements in fields like social networks, biological systems, and fraud detection.
Current limitations in capturing dynamic dependencies due to fixed temporal decay or structural propagation depths are addressed, allowing for more adaptive and generalizable AI representations.
- · AI researchers
- · Graph neural network developers
- · Companies with dynamic graph data (e.g., social media, finance, biotech)
- · Autonomous agent developers
- · AI models reliant on simplistic temporal/structural assumptions
- · Legacy graph analysis techniques
Improved performance and efficiency of AI systems operating on dynamic graph data.
Faster development and deployment of robust AI agents and predictive models in complex, evolving environments.
Potential for new classes of AI applications that can autonomously adapt to and learn from highly fluid data structures.
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Read at arXiv cs.LG