发表机构
University of Memphis（密苏里大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对时间图基准稀缺问题，SAGA系统通过四阶段管道生成大规模语义丰富的时间图，其架构解耦结构与语义，能实现结构真实性、语义丰富性和自动异常标注，在单个H100 GPU上高效生成大量带受控异常的时间边。
AI 中文摘要
高质量且带有丰富语义和真实异常标签的时间图基准对于训练图神经网络至关重要，但由于隐私限制和标注成本而稀缺。我们提出了SAGA系统，通过四阶段管道生成大规模、语义丰富的时间图。其骨架优先、语义其次的架构将结构与语义解耦：骨架生成器产生幂律图，调度器划分时间块，LLM智能体注入领域语义，状态对齐引擎解决冲突并产生异常标签。与其他方法不同，SAGA在统一框架中实现了结构真实性、语义丰富性和自动异常标注。在单个H100 GPU上，SAGA在90分钟内生成500,000个带受控异常的时间边，可扩展到100,000个节点，聚类系数高于0.99，还支持实时管道可视化等。
英文摘要
High quality temporal graph benchmarks with rich semantics and ground-truth anomaly labels are essential for training graph neural networks, yet remain scarce due to privacy constraints and annotation costs. We present SAGA (Synthetic Agentic Graph Architecture), a system for generating large-scale, semantically rich temporal graphs via a four-phase pipeline. Our Skeleton-First, Semantics-Second architecture decouples structure from semantics: (S) an O(1)-per-edge skeleton generator produces power-law graphs; (A) a dispatcher partitions causally ordered time blocks for parallel execution; (G) LLM agents inject domain semantics using RAG-based rule bases across four domains; and (A) a state alignment engine resolves conflicts via temporal replay, yielding anomaly labels as natural byproducts. Unlike structural generators (e.g., LDBC SNB, Kronecker/R-MAT) or purely LLM-based approaches, SAGA achieves structural realism, semantic richness, and automatic anomaly labeling in a unified framework. On a single H100 GPU with vLLM batching, SAGA generates 500,000 temporal edges with controlled anomalies in under 90 minutes, scaling to 100,000 nodes while maintaining clustering coefficients above 0.99. The system supports real-time pipeline visualization, interactive multi-domain tuning (Finance/AML, Network/IDS, Cyber/APT, Transportation), and a CLI for large-scale GPU-based experiments.
