8 papers
G2rammar: Bilingual Grammar Modeling for Enhanced Text-attributed Graph Learning
Heng Zheng, Haochen You, Zijun Liu +5
Text-attributed graphs require models to effectively integrate both structural topology and semantic content. Recent approaches apply large language models to graphs by linearizing…
GraphGeo: Multi-Agent Debate Framework for Visual Geo-localization with Heterogeneous Graph Neural Networks
Heng Zheng, Yuling Shi, Xiaodong Gu +6
Visual geo-localization requires extensive geographic knowledge and sophisticated reasoning to determine image locations without GPS metadata. Traditional retrieval methods are con…
Empowering LLMs with Structural Role Inference for Zero-Shot Graph Learning
Heng Zhang, Jing Liu, Jiajun Wu +8
Large Language Models have emerged as a promising approach for graph learning due to their powerful reasoning capabilities. However, existing methods exhibit systematic performance…
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…
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…
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…