Diffusion models have demonstrated effectiveness in generating natural images and have been extended to generate diverse data types, including graphs. This new generation of diffusion-based graph generative models has demonstrated significant performance improvements over methods that rely on variational autoencoders or generative adversarial networks. It's important to recognize, however, that most of these models employ Gaussian or categorical diffusion processes, which can struggle with sparse and long-tailed data distributions. In our work, we introduce Graph Beta Diffusion (GBD), a diffusion-based generative model particularly adept at capturing diverse graph structures. GBD utilizes a beta diffusion process, tailored for the sparse and range-bounded characteristics of graph adjacency matrices. Furthermore, we have developed a modulation technique that enhances the realism of the generated graphs by stabilizing the generation of critical graph structures, while preserving flexibility elsewhere. The outstanding performance of GBD across three general graph benchmarks and two biochemical graph benchmarks highlights its capability to effectively capture the complexities of real-world graph data. The code will be made available at https://github.com/YH-UtMSB/Graph_Beta_Diffusion

我们的研究引入了Graph Beta Diffusion（GBD），一种特别擅长捕捉多样化图结构的扩散生成模型。GBD利用了适用于图邻接矩阵的稀疏和有界特征的Beta扩散过程，并开发了调制技术来增强生成图的现实感，同时在其他地方保持灵活性。GBD在三个通用图基准和两个生物化学图基准中的出色性能突显了其有效捕捉现实世界图数据复杂性的能力。

通过Beta扩散推进图生成