Explore a comprehensive lecture on score-based generative modeling of graphs using stochastic differential equations. Delve into the challenges of graph generation and learn about a novel approach that overcomes limitations of previous methods. Examine the proposed graph diffusion process, which models joint distribution of nodes and edges through a system of SDEs. Discover new score matching objectives and an efficient solver for sampling from the reverse diffusion process. Analyze the model's performance on diverse datasets, including molecule generation. Investigate the forward and reverse diffusion processes, various GDSS variants, and the design of score-based models. Conclude with a discussion on model limitations and potential future directions in this cutting-edge field of graph generation.