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Evidence record 11997 · automatically gathered

SAGA: Synthetic Agentic Graph Architecture for Temporal Benchmark Generation

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

Record details

Published: 19 July 2026
Source: arXiv
Category: Research
Topics: Privacy · Agents & autonomy
Retrieved: 21 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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How to cite this record

ethics.ai (19 July 2026), “SAGA: Synthetic Agentic Graph Architecture for Temporal Benchmark Generation,” evidence record 11997, https://ethics.ai/record/11997 (originally published by arXiv).

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