用AI代理自动修复文本图数据质量问题，提升分析可靠性
When LLM Agents Meet Graph Optimization: An Automated Data Quality Improvement Approach
- 设计多智能体系统，自动检测并优化文本图的文本、结构和标签缺陷
- 在5个数据集上9种场景测试，显著提升各类图神经网络性能
- 适合关注数据质量、图学习与大模型融合应用的研究者
文本属性图（TAGs）作为现代数据管理与分析中的关键图结构数据，结合了结构关系与丰富文本语义，广泛应用于各类场景。然而，分析模型（尤其是图神经网络，GNNs）的性能高度依赖数据质量。实证分析表明，传统及基于大语言模型（LLM）增强的GNN在文本、结构和标签缺陷下均出现明显退化，凸显文本图质量是可靠分析的关键瓶颈。现有研究虽探索过数据级优化，但多针对单一缺陷类型，仅聚焦结构或标签等局部方面，缺乏系统性与全面性。为此，我们提出LAGA（Large Language and Graph Agent），一个统一的多智能体框架，实现对文本图质量的全面优化。LAGA将图质量控制建模为以数据为中心的自动化流程，集成检测、规划、执行与评估智能体，形成闭环。通过协同多模态优化，全面改善文本、结构与标签质量。在5个数据集、16个基线、9种场景下的大量实验验证了LAGA的有效性、鲁棒性与可扩展性，证实数据驱动的质量优化对可靠文本图分析至关重要。
原文摘要 · Abstract (English)
Text-attributed graphs (TAGs) have become a key form of graph-structured data in modern data management and analytics, combining structural relationships with rich textual semantics for diverse applications. However, the effectiveness of analytical models, particularly graph neural networks (GNNs), is highly sensitive to data quality. Our empirical analysis shows that both conventional and LLM-enhanced GNNs degrade notably under textual, structural, and label imperfections, underscoring TAG quality as a key bottleneck for reliable analytics. Existing studies have explored data-level optimization for TAGs, but most focus on specific degradation types and target a single aspect like structure or label, lacking a systematic and comprehensive perspective on data quality improvement. To address this gap, we propose LAGA (Large Language and Graph Agent), a unified multi-agent framework for comprehensive TAG quality optimization. LAGA formulates graph quality control as a data-centric process, integrating detection, planning, action, and evaluation agents into an automated loop. It holistically enhances textual, structural, and label aspects through coordinated multi-modal optimization. Extensive experiments on 5 datasets and 16 baselines across 9 scenarios demonstrate the effectiveness, robustness and scalability of LAGA, confirming the importance of data-centric quality optimization for reliable TAG analytics.
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