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该论文研究在图数据库中直接进行图神经网络的训练与推理，展示使用Cypher执行采样、特征检索和完整前向传播的可行性，优化后训练速度提升约2.5倍，推理速度比冷加载图到内存快10倍以上。
A talk from WeAreDevelopers World Congress 2026 on modeling organizations as graphs to reveal hidden networks where work actually happens, alongside related videos on graph databases, knowledge graphs, and developer conferences.
CYGNET is a pre-execution gate system that validates and corrects Cypher queries generated by LLM agents over knowledge graphs, catching structural failures before they hit production databases with near-zero false positives and achieving 81–95% success in repairing broken queries across five language models.
This paper introduces Reflection-Augmented Scaling (RAS), a method that uses execution feedback from failed Cypher queries to iteratively refine query generation via in-context learning, reducing execution error rates by 41-50% across multiple datasets and models.