  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. In contrast, we discover that decomposing these edges into distinct
semantic relations significantly enhances the performance of GNNs. Despite
this, manually identifying and labeling of edges to corresponding semantic
relations is labor-intensive, often requiring domain expertise. To this end, we
introduce RoSE (Relation-oriented Semantic Edge-decomposition), a novel
framework that leverages the capability of Large Language Models (LLMs) to
decompose the graph structure by analyzing raw text attributes - in a fully
automated manner. RoSE operates in two stages: (1) identifying meaningful
relations using an LLM-based generator and discriminator, and (2) categorizing
each edge into corresponding relations by analyzing textual contents associated
with connected nodes via an LLM-based decomposer. Extensive experiments
demonstrate that our model-agnostic framework significantly enhances node
classification performance across various datasets, with improvements of up to
16% on the Wisconsin dataset.
