{
  "id": 11997,
  "url": "https://arxiv.org/abs/2607.17288v1",
  "title": "SAGA: Synthetic Agentic Graph Architecture for Temporal Benchmark Generation",
  "summary": "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 pow",
  "authors": "Jiacheng Ding, Xiaofei Zhang",
  "category": "research",
  "topics": "privacy-surveillance,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-19T15:09:21.000Z",
  "fetched_at": "2026-07-21T05:10:12.656Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/11997",
  "original_url": "https://arxiv.org/abs/2607.17288v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}