Generating graph-structured data requires learning the underlying
distribution of graphs. Yet, this is a challenging problem, and the previous
graph generative methods either fail to capture the permutation-invariance
property of graphs or cannot sufficiently model the complex dependency between
nodes and edges, which is crucial for generating real-world graphs such as
molecules. To overcome such limitations, we propose a novel score-based
generative model for graphs with a continuous-time framework. Specifically, we
propose a new graph diffusion process that models the joint distribution of the
nodes and edges through a system of stochastic differential equations (SDEs).
Then, we derive novel score matching objectives tailored for the proposed
diffusion process to estimate the gradient of the joint log-density with
respect to each component, and introduce a new solver for the system of SDEs to
efficiently sample from the reverse diffusion process. We validate our graph
generation method on diverse datasets, on which it either achieves
significantly superior or competitive performance to the baselines. Further
analysis shows that our method is able to generate molecules that lie close to
the training distribution yet do not violate the chemical valency rule,
demonstrating the effectiveness of the system of SDEs in modeling the node-edge
relationships. Our code is available at this https URL

提出了一种基于得分的图生成模型，采用连续时间框架下的新图扩散过程，通过随机微分方程系统对节点和边缘进行联合分布建模，并提出了适用于该过程的新颖得分匹配目标，通过求解反向扩散过程的方程系统高效采样。通过对多个数据集的验证，该方法在生成具有挑战性的现实世界图形时获得了优异的性能，并能够生成符合 训练分布的分子，表明其对于节点 - 边缘关系的建模具有有效性。