Deep Semantic Graph Learning via LLM based Node Enhancement

• URL: http://arxiv.org/abs/2502.07982v1
• Date: Tue, 11 Feb 2025 21:55:46 GMT
• Title: Deep Semantic Graph Learning via LLM based Node Enhancement
• Authors: Chuanqi Shi, Yiyi Tao, Hang Zhang, Lun Wang, Shaoshuai Du, Yixian Shen, Yanxin Shen,
• Abstract summary: Large Language Models (LLMs) have demonstrated superior capabilities in understanding text semantics. This paper proposes a novel framework that combines Graph Transformer architecture with LLM-enhanced node features.
• Score: 5.312946761836463
• License:
• Abstract: 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.

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