5 papers · 1 filter
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…
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…
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…
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…
GraphTracer: Graph-Guided Failure Tracing in LLM Agents for Robust Multi-Turn Deep Search
Heng Zhang, Yuling Shi, Xiaodong Gu +5
Multi-agent systems powered by Large Language Models excel at complex tasks through coordinated collaboration, yet they face high failure rates in multi-turn deep search scenarios.…