GraphText: Graph Reasoning in Text Space
Jianan Zhao Le Zhuo Yikang Shen Meng Qu Kai Liu Michael Bronstein Zhaocheng Zhu Jian Tang
Large Language Models (LLMs) have gained the ability to assimilate human
knowledge and facilitate natural language interactions with both humans and
other LLMs. However, despite their impressive achievements, LLMs have not made
significant advancements in the realm of graph machine learning. This
limitation arises because graphs encapsulate distinct relational data, making
it challenging to transform them into natural language that LLMs understand. In
this paper, we bridge this gap with a novel framework, GraphText, that
translates graphs into natural language. GraphText derives a graph-syntax tree
for each graph that encapsulates both the node attributes and inter-node
relationships. Traversal of the tree yields a graph text sequence, which is
then processed by an LLM to treat graph tasks as text generation tasks.
Notably, GraphText offers multiple advantages. It introduces training-free
graph reasoning: even without training on graph data, GraphText with ChatGPT
can achieve on par with, or even surpassing, the performance of
supervised-trained graph neural networks through in-context learning (ICL).
Furthermore, GraphText paves the way for interactive graph reasoning, allowing
both humans and LLMs to communicate with the model seamlessly using natural
language. These capabilities underscore the vast, yet-to-be-explored potential
of LLMs in the domain of graph machine learning.