  Large language models (LLMs) have achieved great success in many fields, and
recent works have studied exploring LLMs for graph discriminative tasks such as
node classification. However, the abilities of LLMs for graph generation remain
unexplored in the literature. Graph generation requires the LLM to generate
graphs with given properties, which has valuable real-world applications such
as drug discovery, while tends to be more challenging. In this paper, we
propose LLM4GraphGen to explore the ability of LLMs for graph generation with
systematical task designs and extensive experiments. Specifically, we propose
several tasks tailored with comprehensive experiments to address key questions
regarding LLMs' understanding of different graph structure rules, their ability
to capture structural type distributions, and their utilization of domain
knowledge for property-based graph generation. Our evaluations demonstrate that
LLMs, particularly GPT-4, exhibit preliminary abilities in graph generation
tasks, including rule-based and distribution-based generation. We also observe
that popular prompting methods, such as few-shot and chain-of-thought
prompting, do not consistently enhance performance. Besides, LLMs show
potential in generating molecules with specific properties. These findings may
serve as foundations for designing good LLMs based models for graph generation
and provide valuable insights and further research.
