# Deep Semantic Graph Learning via LLM based Node Enhancement

arXiv: https://arxiv.org/abs/2502.07982 · HTML: https://mlsanity.com/paper/2502.07982/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 17 references that are in the map.
- First posted on arXiv 11 Feb 2025.

## Builds on (most cited first, 10 of 10)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [Code Llama: Open Foundation Models for Code](https://mlsanity.com/paper/2308.12950/) (2023)
- [A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Ugly](https://mlsanity.com/paper/2312.02003/) (2023)
- [Exploring the Potential of Large Language Models (LLMs) in Learning on Graphs](https://mlsanity.com/paper/2307.03393/) (2023)
- [BERT: A Review of Applications in Natural Language Processing and Understanding](https://mlsanity.com/paper/2103.11943/) (2021)
- [Deep Sentence Embedding Using Long Short-Term Memory Networks: Analysis and Application to Information Retrieval](https://mlsanity.com/paper/1502.06922/) (2015)
- [The FacT: Taming Latent Factor Models for Explainability with Factorization Trees](https://mlsanity.com/paper/1906.02037/) (2019)
- [Bridging Context Gaps: Leveraging Coreference Resolution for Long Contextual Understanding](https://mlsanity.com/paper/2410.01671/) (2024)

## Built on by (most cited first, 8 of 8)

- [Enhancing Customer Contact Efficiency with Graph Neural Networks in Credit Card Fraud Detection Workflow](https://mlsanity.com/paper/2504.02275/) (2025)
- [Research and Design on Intelligent Recognition of Unordered Targets for Robots Based on Reinforcement Learning](https://mlsanity.com/paper/2503.07340/) (2025)
- [A Narrative Review on Large AI Models in Lung Cancer Screening, Diagnosis, and Treatment Planning](https://mlsanity.com/paper/2506.07236/) (2025)
- [Industrial Internet Robot Collaboration System and Edge Computing Optimization](https://mlsanity.com/paper/2504.02492/) (2025)
- [GraphRAG for Engineering Diagrams: ChatP&ID Enables LLM Interaction with P&IDs](https://mlsanity.com/paper/2603.22528/) (2026)
- [Slang Context-based Inference Enhancement via Greedy Search-Guided Chain-of-Thought Prompting](https://mlsanity.com/paper/2603.13230/) (2026)
- [Beyond the Aggregation Dilemma: Prior-Retaining Decoupled Learning for Multimodal Graphs](https://mlsanity.com/paper/2605.24684/) (2026)
- [TemporalGraphLLM: Temporal Graph Neural Networks with Large Language Models for Dynamic Text-Attributed Graphs](https://mlsanity.com/paper/2609.31881/) (2026)

## Abstract (arXiv)

Graph learning has attracted significant attention due to its widespread real-world applications. Current mainstream approaches rely on text node features and obtain initial node embeddings through shallow embedding learning using GNNs, which shows limitations in capturing deep textual semantics. Recent advances in Large Language Models (LLMs) have demonstrated superior capabilities in understanding text semantics, transforming traditional text feature processing. This paper proposes a novel framework that combines Graph Transformer architecture with LLM-enhanced node features. Specifically, we leverage LLMs to generate rich semantic representations of text nodes, which are then processed by a multi-head self-attention mechanism in the Graph Transformer to capture both local and global graph structural information. Our model utilizes the Transformer's attention mechanism to dynamically aggregate neighborhood information while preserving the semantic richness provided by LLM embeddings. Experimental results demonstrate that the LLM-enhanced node features significantly improve the performance of graph learning models on node classification tasks. This approach shows promising results across multiple graph learning tasks, offering a practical direction for combining graph networks with language models.