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cloud.google.com·7h ago
AI Research
arXiv CS.AI·27d ago·5 min read
ProtoGuide: Prototype-Driven Guidance for Class-Conditional Graph Generation
TL;DR
ProtoGuide is a novel framework for class-conditional graph generation that enhances performance by using prototype-driven guidance. It significantly improves classification accuracy on various real-world networks, achieving up to 83.8%.
✦ Why It Matters
Implement ProtoGuide to enhance your graph generation models and improve classification accuracy today.
Key Takeaways
- Use ProtoGuide to improve graph generation performance.
- Leverage prototype-driven guidance for better classification accuracy.
- Explore the framework's flexibility with different model backbones.
- Test the robustness of your models with small support sets.
How It Works
ProtoGuide operates by relaxing the denoiser's per-edge output into a differentiable soft adjacency matrix. It then uses a frozen Siamese graph neural network to score this output against target-class prototypes, allowing for gradient injection back into the denoiser.
This process enables effective class-conditional guidance without retraining the model.