AIGC for Graphs: Current Techniques and Future Trends

Hanchen Wang, Dawei Cheng, Ying Zhang, Wenjie Zhang

Abstract: As artificial intelligence technology continues to advance, artificial intelligence-generated content (AIGC) has begun to evolve towards generating complex and structured data, particularly graph data. As an important topic in many fields such as database, data mining, and machine learning, graph generation holds significant value for simulating complex relationships between entities and has shown vast potential for applications in fields such as molecular generation, drug design, and material discovery. In this context, AIGC technology for graph generation has received widespread attention. This tutorial outlines the latest developments in AIGC for graph generation. We categorize existing methods into two main types according to their objectives and motivations: similarity-based generation and function-driven generation. We first provide an overview of AIGC models for graph generation. Then, we conduct a thorough review of the existing works. Finally, we explore the current trends and future directions, discussing potential ways to integrate database and machine learning techniques for graph generation.

External IDs:dblp:conf/icde/WangCZZ25