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20242026
most citedUniGLM: Training One Unified Language Model for Text-Attributed Graph Embedding

2 citations · 3 across the 8 of their papers we have counts for

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cs.LG2024

GAugLLM: Improving Graph Contrastive Learning for Text-Attributed Graphs with Large Language Models

Yi Fang, Dongzhe Fan, Daochen Zha +1

This work studies self-supervised graph learning for text-attributed graphs (TAGs) where nodes are represented by textual attributes. Unlike traditional graph contrastive methods t…

cs.CL2024★ 2 cited

UniGLM: Training One Unified Language Model for Text-Attributed Graph Embedding

Yi Fang, Dongzhe Fan, Sirui Ding +2

Representation learning on text-attributed graphs (TAGs), where nodes are represented by textual descriptions, is crucial for textual and relational knowledge systems and recommend…