{
  "annotation_id": "4d00c999-e4ce-4f11-b1b0-adc8e34419c4",
  "date_created": "2026-03-02T18:00:57.340000Z",
  "date_modified": "2026-03-02T18:00:57.340000Z",
  "file_hash": "08132893eec4599d71e18a6fc58eb4a05e81be9781d5869bc4267c2dd152093d",
  "private": false,
  "record": {
    "abstract": "We study spatial embeddings of random graphs in which nodes are randomly\ndistributed in geographical space. We let the edge probability between any two\nnodes to be dependent on the spatial distance between them and demonstrate that\nthis model captures many generic properties of social networks, including the\n``small-world\u0027\u0027 properties, skewed degree distribution, and most distinctively\nthe existence of community structures.",
    "arxiv_id": "physics/0505128",
    "authors": [
      "Ling Heng Wong",
      "Philippa Pattison",
      "Garry Robins"
    ],
    "categories": [
      "physics.soc-ph"
    ],
    "doi": "10.1016/j.physa.2005.04.029",
    "title": "A spatial model for social networks",
    "url": "https://arxiv.org/abs/physics/0505128"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "6be301a9-9c1f-41d2-8ad4-8d64ab74dfff",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}