LLM Enhancers for GNNs: An Analysis from the Perspective of Causal Mechanism Identification
Hang Gao, Wenxuan Huang, Fengge Wu, Junsuo Zhao, Changwen Zheng, Huaping Liu·May 13, 2025
Summary
A paper explores using large language models to boost graph neural networks in representation learning. It introduces an Attention Transfer (AT) module, validates it across datasets, and shows improvements with models like Llama2, Qwen2, and Llama3. The AT component optimizes by selecting and transmitting optimal information. The study also discusses creating semantically rich node features for GNNs using Wikipedia entries and LLM Enhancers. It examines various topological structures, node connections, and accuracy results for different q values and prompts. The document analyzes node-level and graph-level experiments, highlighting the impact of hidden dimension size on accuracy.
Introduction
Background
Overview of Graph Neural Networks (GNNs) and their role in representation learning
Importance of large language models in natural language processing and their potential in GNNs
Objective
To explore the integration of large language models (LLMs) with GNNs for representation learning enhancement
To introduce and validate an Attention Transfer (AT) module for optimizing information transmission in GNNs
Method
Data Collection
Gathering datasets suitable for GNNs and LLMs
Data Preprocessing
Preprocessing steps for integrating LLMs with GNNs
Attention Transfer (AT) Module
Design and implementation of the AT module
Mechanism of selecting and transmitting optimal information
LLM Enhancers
Utilization of LLMs to create semantically rich node features for GNNs
Topological Structures and Node Connections
Examination of various topological structures and their impact on GNN performance
Accuracy Analysis
Evaluation of GNN performance with and without the AT module
Analysis of accuracy results for different q values and prompts
Results
Node-Level Experiments
Detailed analysis of node-level experiments
Impact of hidden dimension size on node-level accuracy
Graph-Level Experiments
Overview of graph-level experiments
Comparative analysis of GNN performance across different datasets
Discussion
Impact of Hidden Dimension Size
Analysis of the effect of hidden dimension size on GNN accuracy
Semantically Rich Node Features
Evaluation of the effectiveness of semantically rich node features created by LLM Enhancers
AT Module Performance
Assessment of the AT module's contribution to GNN performance enhancement
Conclusion
Summary of Findings
Recap of the study's main findings
Implications
Discussion on the implications of using LLMs in GNNs for representation learning
Future Work
Suggestions for future research directions in the integration of LLMs with GNNs
Basic info
papers
machine learning
artificial intelligence
Advanced features
Insights
What limitations are identified in the study regarding the use of LLMs and GNNs for representation learning?
What is the primary objective of using large language models in conjunction with graph neural networks as discussed in the paper?
How does the Attention Transfer module function to enhance representation learning in GNNs?
What are the key innovations introduced by the Attention Transfer module in the context of GNNs?