3 papers
cs.LG2025
GraphShaper: Geometry-aware Alignment for Improving Transfer Learning in Text-Attributed Graphs
Heng Zhang, Tianyi Zhang, Yuling Shi +6
Graph foundation models represent a transformative paradigm for learning transferable representations across diverse graph domains. Recent methods leverage large language models to…
cs.LG2025
H4G: Unlocking Faithful Inference for Zero-Shot Graph Learning in Hyperbolic Space
Heng Zhang, Tianyi Zhang, Zijun Liu +6
Text-attributed graphs are widely used across domains, offering rich opportunities for zero-shot learning via graph-text alignment. However, existing methods struggle with tasks re…
cs.GR2025
Can Representation Gaps Be the Key to Enhancing Robustness in Graph-Text Alignment?
Heng Zhang, Tianyi Zhang, Yuling Shi +6
Representation learning on text-attributed graphs (TAGs) integrates structural connectivity with rich textual semantics, enabling applications in diverse domains. Current methods l…