Large Language Models as Topological Structure Enhancers for
  Text-Attributed Graphs
Shengyin Sun Yuxiang Ren Chen Ma Xuecang Zhang
The latest advancements in large language models (LLMs) have revolutionized
the field of natural language processing (NLP). Inspired by the success of LLMs
in NLP tasks, some recent work has begun investigating the potential of
applying LLMs in graph learning tasks. However, most of the existing work
focuses on utilizing LLMs as powerful node feature augmenters, leaving
employing LLMs to enhance graph topological structures an understudied problem.
In this work, we explore how to leverage the information retrieval and text
generation capabilities of LLMs to refine/enhance the topological structure of
text-attributed graphs (TAGs) under the node classification setting. First, we
propose using LLMs to help remove unreliable edges and add reliable ones in the
TAG. Specifically, we first let the LLM output the semantic similarity between
node attributes through delicate prompt designs, and then perform edge deletion
and edge addition based on the similarity. Second, we propose using
pseudo-labels generated by the LLM to improve graph topology, that is, we
introduce the pseudo-label propagation as a regularization to guide the graph
neural network (GNN) in learning proper edge weights. Finally, we incorporate
the two aforementioned LLM-based methods for graph topological refinement into
the process of GNN training, and perform extensive experiments on four
real-world datasets. The experimental results demonstrate the effectiveness of
LLM-based graph topology refinement (achieving a 0.15%--2.47% performance gain
on public benchmarks).