{
  "id": 13858733,
  "title": "When Edge Independence Fails: Joint Graph Diffusion with Latent Sociability Priors",
  "url": "https://urgent.news/2026/10/09/when-edge-independence-fails-joint-graph-diffusion-with-latent",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-09T15:50:48.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2610.13765v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Discrete graph diffusion models corrupt graph structure through independent edge updates with a Markovian noise model. Combined with the exchangeability requirement on the output distribution, the terminal prior is necessarily an Erdős--Rényi (E-R) random graph. This has a fundamental consequence for unconditional graph generation without node features: in the large graph limit, any GNN denoiser…",
  "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."
}