{
  "id": 10615516,
  "title": "A Unified Uncertainty Representation for Graph Neural Networks via Doubly-Spectral Stochastic Expansion",
  "url": "https://urgent.news/2026/09/28/a-unified-uncertainty-representation-for-graph-neural-networks-via",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-28T17:42:52.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2609.35703v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Reliable deployment of graph neural networks requires calibration, out-of-distribution (OOD) detection, and robustness to distribution shift, yet existing methods address these needs with separate models and objectives. We model uncertain node embeddings as random graph signals: graph Fourier filters capture structural variation, and a scalar orthogonal-polynomial chaos coordinate captures latent…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}