Integrating Structural and Semantic Signals in Text-Attributed Graphs with BiGTex

Understand

Text-attributed graphs (TAGs) present unique challenges in representation learning by requiring models to capture both the semantic richness of node-associated texts and the structural dependencies of the graph.

• While graph neural networks (GNNs) excel at modeling topological information, they lack the capacity to process unstructured text.
• Conversely, large language models (LLMs) are proficient in text understanding but are typically unaware of graph structure.
• In this work, we propose BiGTex (Bidirectional Graph Text), a novel architecture that tightly integrates GNNs and LLMs through stacked Graph-Text Fusion Units.