SAGA: Synthetic Agentic Graph Architecture for Temporal Benchmark Generation
SAGA creates large-scale temporal graphs with ground-truth anomaly labels using a four-phase pipeline. It decouples graph structure generation from semantic injection via LLM agents and RAG rule bases. This system automatically generates realistic, complex datasets for training graph neural networks by deriving anomaly labels from logical conflicts during a replay phase.