Leveraging Discrete Semantic Prototypes for Representation Learning on Text-Attributed Graphs

Integrating Large Language Models (LLMs) with Graph Neural Networks (GNNs) has emerged as a dominant paradigm for representation learning on Text-Attributed Graphs (TAGs). Existing approaches face three primary challenges: the difficulty in distinguishing core semantic information from textual redun...

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書誌詳細

主要な著者:	Yiming Cao, Cheng Peng, Le-Minh Nguyen
フォーマット:	Artigo
言語:	Inglês
出版事項:	IEEE 2026-01-01
シリーズ:	IEEE Access
主題:	Graph representation learning large language model text-attributed graphs vector quantization
オンライン･アクセス:	https://ieeexplore.ieee.org/document/11373041/
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