{
  "annotation_id": "a5ee3f99-6ab3-46ef-9573-dfce5136102f",
  "date_created": "2026-03-02T18:01:35.820000Z",
  "date_modified": "2026-03-02T18:01:35.820000Z",
  "file_hash": "cc214d79b7c8060285a18e4602240a9bf1d95a40b0f478bc14cb56119276fbc5",
  "private": false,
  "record": {
    "abstract": "We propose a growing network model that consists of two tunable mechanisms:\ngrowth by merging modules which are represented as complete graphs and a\nfitness-driven preferential attachment. Our model exhibits the three prominent\nstatistical properties are widely shared in real biological networks, for\nexample gene regulatory, protein-protein interaction, and metabolic networks.\nThey retain three power law relationships, such as the power laws of degree\ndistribution, clustering spectrum, and degree-degree correlation corresponding\nto scale-free connectivity, hierarchical modularity, and disassortativity,\nrespectively. After making comparisons of these properties between model\nnetworks and biological networks, we confirmed that our model has inference\npotential for evolutionary processes of biological networks.",
    "arxiv_id": "q-bio/0611014",
    "authors": [
      "Kazuhiro Takemoto",
      "Chikoo Oosawa"
    ],
    "categories": [
      "q-bio.MN",
      "cond-mat.dis-nn"
    ],
    "journal_ref": "Mathematical Biosciences 208, 454 (2007)",
    "title": "Modeling for evolving biological networks with scale-free connectivity,   hierarchical modularity, and disassortativity",
    "url": "https://arxiv.org/abs/q-bio/0611014"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "5c2a5366-53b5-4d0c-9fa4-909e68c4b6f9",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}