arXiv:2510.11339v2 Announce Type: replace Abstract: Real-world graph typically evolve via a series of events, modeling dynamic interactions between objects across various domains. For dynamic graph learning, dynamic graph neural networks (DGNNs) have emerged as popular solutions. Recently, prompt learning methods have been explored on dynamic graphs. However, existing methods generally focus on capturing the relationship between nodes and time, while overlooking the impact of historical events. In this paper, we propose EVP, an event-aware dynamic graph prompt learning framework that can serve
The continuous evolution of real-world graphs necessitates more sophisticated AI models capable of processing dynamic interactions, driving innovations in prompt learning for dynamic graph neural networks.
This development improves how AI systems model complex, evolving relationships, which is critical for applications ranging from social networks to biological interactions and potentially autonomous agents.
The explicit incorporation of historical events into dynamic graph learning models enhances their ability to predict and understand evolving systems, moving beyond simple node-time relationships.
- · AI/ML researchers
- · Companies with dynamic data streams
- · SaaS platforms that integrate AI
- · Traditional static graph analysis methods
- · AI models lacking dynamic event awareness
Improved performance and accuracy of AI models operating on dynamic graph data.
Accelerated development of more adaptive and context-aware AI agents capable of understanding real-time changes.
New classes of AI applications that can proactively respond to complex, evolving events in various domains.
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Read at arXiv cs.LG