{
  "annotation_id": "d322c0bf-69df-4ff7-b2ae-041464705db8",
  "date_created": "2026-03-02T18:00:57.334000Z",
  "date_modified": "2026-03-02T18:00:57.334000Z",
  "file_hash": "da99f85471c1840c0091f0c7cab02ba55d7dd26311d6c301af10486d6660e71e",
  "private": false,
  "record": {
    "abstract": "We introduce and study a general model of social network formation and\nevolution based on the concept of preferential link formation between similar\nnodes and increased similarity between connected nodes. The model is studied\nnumerically and analytically for three definitions of similarity. In common\nwith real-world social networks, we find coexistence of high and low\nconnectivity phases and history dependence. We suggest that the positive\nfeedback between linking and similarity which is responsible for the model\u0027s\nbehaviour is also an important mechanism in real social networks.",
    "arxiv_id": "physics/0504124",
    "authors": [
      "George C. M. A. Ehrhardt",
      "Matteo Marsili",
      "Fernando Vega-Redondo"
    ],
    "categories": [
      "physics.soc-ph",
      "cond-mat.stat-mech"
    ],
    "title": "Emergence and resilience of social networks: a general theoretical   framework",
    "url": "https://arxiv.org/abs/physics/0504124"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "01ae67de-786c-4669-addf-f1d48ffc75c4",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}