Dimensions overall score 7.0

• Most existing graph diffusion models have significant bias problems that degrade generation quality.technical 90%
• The forward diffusion's maximum perturbation distribution in most models deviates from standard Gaussian distribution while reverse sampling starts from standard Gaussian, creating reverse-starting bias.technical 85%
• The paper proposes a new Langevin sampling algorithm to mitigate reverse-starting bias by aligning with forward maximum perturbation distribution.method 90%
• The paper introduces a score correction mechanism based on a newly defined score difference to address exposure bias.method 90%
• The proposed approach requires no network modifications to existing diffusion models.method 85%
• The approach achieves state-of-the-art results across multiple models, datasets, and tasks.result 80%
• The combination of reverse-starting bias and inherent exposure bias results in degraded generation quality in graph diffusion models.technical 80%

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