@inproceedings{
mercatali2024diffusion,
title={Diffusion Twigs with Loop Guidance for Conditional Graph Generation},
author={Giangiacomo Mercatali and Yogesh Verma and Andre Freitas and Vikas Garg},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=fvOCJAAYLx}
}
We introduce a novel score-based diffusion framework named  Twigs that incorporates multiple co-evolving flows for enriching conditional generation tasks. Specifically, a central or trunk diffusion process is associated with a primary variable (e.g., graph structure), and additional offshoot or  stem processes are dedicated to dependent variables (e.g., graph properties or labels). A new strategy, which we call loop guidance, effectively orchestrates the flow of information between the trunk and the stem processes during sampling. This approach allows us to uncover intricate interactions and dependencies, and unlock new generative capabilities. We provide extensive experiments to demonstrate strong performance gains of the proposed method over contemporary baselines in the context of conditional graph generation, underscoring the potential of Twigs in challenging generative tasks such as inverse molecular design and molecular optimization. 
Code is available at https://github.com/Aalto-QuML/Diffusion_twigs.
Diffusion Twigs with Loop Guidance for Conditional Graph Generation
Giangiacomo Mercatali Yogesh Verma Andre Freitas Vikas Garg