Event-Aware Prompt Learning for Dynamic Graphs
📰 ArXiv cs.AI
Learn how to apply event-aware prompt learning to dynamic graphs for improved modeling of real-world interactions
Action Steps
- Apply event-aware prompt learning to dynamic graphs using DGNNs
- Configure prompt learning methods to capture historical event information
- Test the performance of event-aware prompt learning on dynamic graph benchmarks
- Compare the results with traditional prompt learning methods
- Fine-tune the event-aware prompt learning model for specific domains or applications
Who Needs to Know This
Data scientists and AI engineers working with dynamic graph data can benefit from this technique to improve their models' performance and ability to capture complex interactions
Key Insight
💡 Event-aware prompt learning can capture the impact of historical events on dynamic graphs, leading to improved modeling of real-world interactions
Share This
📈 Improve dynamic graph modeling with event-aware prompt learning! 🤖
Key Takeaways
Learn how to apply event-aware prompt learning to dynamic graphs for improved modeling of real-world interactions
Full Article
Title: Event-Aware Prompt Learning for Dynamic Graphs
Abstract:
arXiv:2510.11339v2 Announce Type: replace-cross 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 ev
Abstract:
arXiv:2510.11339v2 Announce Type: replace-cross 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 ev
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