Graph Generative Model

Graph generative models aim to create new graphs that share characteristics with a given dataset, enabling tasks like data augmentation, network analysis, and drug discovery. Recent research emphasizes developing models that generate graphs with specific tunable features, handle large-scale datasets efficiently (often leveraging diffusion models or sparse representations), and incorporate external information like text prompts for more controlled generation. These advancements are significant because they improve the quality and scalability of generated graphs, leading to more robust and applicable tools across various scientific domains and practical applications.