{
  "annotation_id": "13ec62d4-c409-45fa-a560-f72d8b161b0b",
  "date_created": "2026-03-02T18:01:07.270000Z",
  "date_modified": "2026-03-02T18:01:07.270000Z",
  "file_hash": "3120aa6f084d1d759dc23c4675ed541514db3cd49b0eb009c4497718747c85c8",
  "private": false,
  "record": {
    "abstract": "Scale-free networks are characterized by a degree distribution with power-law\nbehavior and have been shown to arise in many areas, ranging from the World\nWide Web to transportation or social networks. Degree distributions of observed\nnetworks, however, often differ from the power-law type and data based\ninvestigations require modifications of the typical scale-free network.\n  We present an algorithm that generates networks in which the skewness of the\ndegree distribution is tuneable by modifying the preferential attachment step\nof the Barabasi-Albert construction algorithm. Skewness is linearly correlated\nwith the maximal degree of the network and, therefore, adequately represents\nthe influence of superspreaders or hubs. By combining our algorithm with work\nof Holme and Kim, we show how to generate networks with skewness gamma and\nclustering coefficient kappa, over a wide range of values.",
    "arxiv_id": "physics/0602152",
    "authors": [
      "C. C. Leary",
      "M. Schwehm",
      "M. Eichner",
      "H. P. Duerr"
    ],
    "categories": [
      "physics.data-an"
    ],
    "doi": "10.1016/j.physa.2007.04.058",
    "title": "Tuning degree distributions of scale-free networks",
    "url": "https://arxiv.org/abs/physics/0602152"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "a64ceead-f888-4909-a642-87276b0f2416",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}