Unleashing the Potential of Text-attributed Graphs: Automatic Relation Decomposition via Large Language Models

Understand

Recent advancements in text-attributed graphs (TAGs) have significantly improved the quality of node features by using the textual modeling capabilities of language models.

• Despite this success, utilizing text attributes to enhance the predefined graph structure remains largely unexplored.
• Our extensive analysis reveals that conventional edges on TAGs, treated as a single relation (e.g., hyperlinks) in previous literature, actually encompass mixed semantics (e.g., "advised by" and "participates in").
• This simplification hinders the representation learning process of Graph Neural Networks (GNNs) on downstream tasks, even when integrated with advanced node features.