Advancing Graph Representation Learning with Large Language Models: A
  Comprehensive Survey of Techniques
Qiheng Mao Zemin Liu Chenghao Liu Zhuo Li Jianling Sun
The integration of Large Language Models (LLMs) with Graph Representation
Learning (GRL) marks a significant evolution in analyzing complex data
structures. This collaboration harnesses the sophisticated linguistic
capabilities of LLMs to improve the contextual understanding and adaptability
of graph models, thereby broadening the scope and potential of GRL. Despite a
growing body of research dedicated to integrating LLMs into the graph domain, a
comprehensive review that deeply analyzes the core components and operations
within these models is notably lacking. Our survey fills this gap by proposing
a novel taxonomy that breaks down these models into primary components and
operation techniques from a novel technical perspective. We further dissect
recent literature into two primary components including knowledge extractors
and organizers, and two operation techniques including integration and training
stratigies, shedding light on effective model design and training strategies.
Additionally, we identify and explore potential future research avenues in this
nascent yet underexplored field, proposing paths for continued progress.