A Systematic Survey on Generative Models for Graph Generation

Graph-structured data underpin a wide range of real-world systems, from molecular chemistry and biological interactions to social and information networks. Recent advancements in deep generative models, along with the emergence of large language models (LLMs), have spurred significant progress in graph generative models (GGMs), enabling the synthesis of complex and realistic graph structures. This article provides a comprehensive overview of the literature in this field. We begin by distinguishing between two primary categories of graph datasets based on their structural formation mechanisms: geometric and scale-free. Building on this foundation, we propose a unified taxonomy that systematically organizes this rapidly evolving landscape by jointly considering (i) the category of generated graphs, (ii) the graph attribute modality, and (iii) the underlying probabilistic modeling paradigm. We then analyze representative neural network architectures and modeling strategies, followed by an overview of evaluation metrics. Finally, we highlight key applications in molecular design, protein optimization, social network analysis, and recommendation systems and outline four promising directions for future research.