Bridging Large Language Models and Graph Structure Learning Models for Robust Representation Learning

Graph representation learning, involving both node features and graph structures, is crucial for real-world applications but often encounters pervasive noise. State-of-the-art methods typically address noise by focusing separately on node features with large language models (LLMs) and on graph struc...

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Bibliographische Detailangaben

Hauptverfasser:	Su, Guangxin, Zhu, Yifan, Zhang, Wenjie, Wang, Hanchen, Zhang, Ying
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Artificial Intelligence Computer Science - Learning
Online-Zugang:	Volltext bestellen
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