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Related Experiment Video

Updated: Jul 7, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Multimodal graph neural network with large language models for node and link prediction.

Bo Peng¹, Huan Xu¹, Xiangjiu Che¹

¹College of Computer Science and Technology, Jilin University, Changchun, Jilin, China.

Frontiers in Artificial Intelligence

|July 6, 2026

Summary

This summary is machine-generated.

This study introduces M2GNN, a graph learning framework combining large language models (LLMs) and graph neural networks (GNNs) to enhance node classification and link prediction. The M2GNN framework effectively integrates semantic information from LLMs with graph structures, improving prediction accuracy.

Keywords:

graph editor graph neural network large language models multimodal oversmoothing

Related Experiment Videos

Last Updated: Jul 7, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Area of Science:

Artificial Intelligence
Machine Learning
Graph Analytics

Background:

Graph neural networks (GNNs) excel at modeling graph-structured data but suffer from over-smoothing, hindering node representation.
Large language models (LLMs) capture semantic context in text but lack graph structural encoding capabilities.

Purpose of the Study:

To propose M2GNN, an LLM-enhanced graph learning framework for node classification and link prediction.
To address limitations of GNNs and LLMs in multimodal graph-text data by integrating semantic and structural information.

Main Methods:

Developed M2GNN, a framework focusing on controlled graph-text prediction.
Utilized LLM-derived semantic embeddings for edge refinement.
Applied stable positional encodings to the refined graph.
Adaptively fused graph-based and language-informed predictors.

Main Results:

M2GNN achieved competitive or improved performance on benchmark datasets for node classification and link prediction.
Ablation analyses confirmed the contributions of semantic edge refinement, stable structural encoding, and graph-language fusion.

Conclusions:

Demonstrated a practical pipeline integrating semantic edge refinement, stable structural encoding, and adaptive graph-language fusion for graph-text prediction.
Combining LLM semantics with structure-aware graph learning enhances prediction robustness within a defined task scope.