{
  "id": 19480,
  "url": "https://arxiv.org/abs/2608.12441v1",
  "title": "Dual Spatial-Temporal Attribution: Architecture-Aligned Post-Hoc Explainability for Recurrent Graph Anomaly Detection",
  "summary": "Deep learning detectors for anomalies in dynamic graphs have reached strong accuracy, yet they remain opaque: when an edge is flagged, the analyst receives a score but no reason. This opacity is untenable in the cooperative, regulated information systems where such detectors are deployed, where automated decisions must be auditable and trustworthy. We address this gap for AddGraph, the foundational GCN+GRU framework for edge-level anomaly detection in dynamic graphs, which to our knowledge has n",
  "authors": "Iyad Assaad Nekka, Hamida Seba, Khaled Walid Hidouci, Karima Amrouche",
  "category": "research",
  "topics": "regulation,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-12T15:58:27.000Z",
  "fetched_at": "2026-08-14T05:10:49.168Z",
  "source_slug": "arxiv-cslg",
  "source_name": "arXiv cs.LG",
  "source_homepage": "https://arxiv.org/list/cs.LG/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/19480",
  "original_url": "https://arxiv.org/abs/2608.12441v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}