Qin, Y., Madeira, M., Thanou, D., & Frossard, P. (2024). DEFOG: DISCRETE FLOW MATCHING FOR GRAPH GENERATION. arXiv preprint arXiv:2410.04263.
This paper introduces DeFoG, a novel framework for graph generation that leverages discrete flow matching (DFM) to address limitations of diffusion-based models, aiming for improved efficiency, flexibility, and performance.
DeFoG employs a flow-based approach with a linear interpolation noising process and a continuous-time Markov chain (CTMC) based denoising process. It utilizes an expressive graph transformer to ensure node permutation equivariance, respecting graph symmetry. The framework decouples training and sampling stages, enabling independent optimization. Algorithmic improvements are introduced for both stages, including alternative initial distributions, modified CTMC rate matrices, and time-adaptive strategies. Theoretical analysis demonstrates DeFoG's ability to faithfully replicate the ground truth distribution for general discrete data, extending to graph data. Experiments are conducted on synthetic and molecular datasets, comparing DeFoG with state-of-the-art diffusion models in terms of training and sampling efficiency, as well as conditional generation on a digital pathology dataset.
DeFoG presents a novel and effective approach for graph generation, surpassing diffusion models in performance and efficiency. Its decoupled design and algorithmic improvements offer enhanced flexibility and optimization capabilities. Theoretical guarantees further solidify DeFoG's foundation, establishing it as a promising framework for various graph generation tasks.
This research significantly contributes to the field of graph generation by introducing a novel DFM-based framework that outperforms existing diffusion models. DeFoG's efficiency, flexibility, and theoretical foundation make it a valuable tool for diverse applications requiring graph generation.
While DeFoG demonstrates promising results, further exploration of its applicability to larger and more complex graph datasets is warranted. Investigating the integration of domain-specific knowledge into the framework could further enhance its performance in specific applications.
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by Yiming Qin, ... at arxiv.org 10-08-2024
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