8 papers
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…
HyperAgent: Leveraging Hypergraphs for Topology Optimization in Multi-Agent Communication
Heng Zhang, Yuling Shi, Xiaodong Gu +5
Recent advances in large language model-powered multi-agent systems have demonstrated remarkable collective intelligence through effective communication. However, existing approach…
D3MAS: Decompose, Deduce, and Distribute for Enhanced Knowledge Sharing in Multi-Agent Systems
Heng Zhang, Yuling Shi, Xiaodong Gu +5
Multi-agent systems powered by large language models exhibit strong capabilities in collaborative problem-solving. However, these systems suffer from substantial knowledge redundan…