Fine-Grained Graph Generation through Latent Mixture Scheduling
Learn to generate graphs with fine-grained structural control using a novel conditional variational autoencoder, enabling applications in drug discovery and social network modeling.
- Implement a conditional variational autoencoder to generate graphs with fine-grained structural control
- Use latent mixture scheduling to refine the decoder's latent space
- Train the model on a dataset of graphs with desired topological properties
- Evaluate the generated graphs using metrics such as graph similarity and property satisfaction
- Apply the approach to real-world applications like drug discovery and social network modeling
Data scientists and ML researchers working on graph generation tasks can benefit from this approach to create graphs with specific topological properties, while software engineers can implement the proposed method in various domains.
💡 Fine-grained graph generation can be achieved through latent mixture scheduling in a conditional variational autoencoder, enabling precise control over graph properties.
🚀 Generate graphs with fine-grained control using a novel conditional VAE! 📈
Key Takeaways
Learn to generate graphs with fine-grained structural control using a novel conditional variational autoencoder, enabling applications in drug discovery and social network modeling.
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Abstract:
arXiv:2605.02780v1 Announce Type: new Abstract: Structure aware graph generation aims to generate graphs that satisfy given topological properties. It has applications in domains such as drug discovery, social network modeling, and knowledge graph construction. Unlike existing methods that only provide coarse control over graph properties, we introduce a novel conditional variational autoencoder for fine-grained structural control in graph generation. The approach refines the decoder's latent sp
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