  Graph analysis is fundamental in real-world applications. Traditional
approaches rely on SPARQL-like languages or clicking-and-dragging interfaces to
interact with graph data. However, these methods either require users to
possess high programming skills or support only a limited range of graph
analysis functionalities. To address the limitations, we propose a large
language model (LLM)-based framework called ChatGraph. With ChatGraph, users
can interact with graphs through natural language, making it easier to use and
more flexible than traditional approaches. The core of ChatGraph lies in
generating chains of graph analysis APIs based on the understanding of the
texts and graphs inputted in the user prompts. To achieve this, ChatGraph
consists of three main modules: an API retrieval module that searches for
relevant APIs, a graph-aware LLM module that enables the LLM to comprehend
graphs, and an API chain-oriented finetuning module that guides the LLM in
generating API chains.
