{
  "annotation_id": "6e059191-8583-4adc-b03c-887898651f67",
  "date_created": "2026-03-02T18:01:10.921000Z",
  "date_modified": "2026-03-02T18:01:10.921000Z",
  "file_hash": "ff70b2f4cbd9ba0c21f9313e14ceb716bf90fde390e1a343019886accc33a8fb",
  "private": false,
  "record": {
    "abstract": "Based on brief review of approaches for community identification and\nmeasurement for sensitivity characterization, the accuracy and precision of\nseveral approaches for detecting communities in weighted networks are\ninvestigated. In weighted networks, the community structure should take both\nlinks and link weights into account and the partition of networks should be\nevaluated by weighted modularity $Q^w$. The results reveal that link weight has\nimportant effects on communities especially in dense networks. Potts model and\nWeighted Extremal Optimization (WEO) algorithm work well on weighted networks.\nThen Potts model and WEO algorithms are used to detect communities in Rhesus\nmonkey network. The results gives nice understanding for real community\nstructure.",
    "arxiv_id": "physics/0607271",
    "authors": [
      "Ying Fan",
      "Menghui Li",
      "Peng Zhang",
      "Jinshan Wu",
      "Zengru Di"
    ],
    "categories": [
      "physics.soc-ph"
    ],
    "doi": "10.1016/j.physa.2006.11.036",
    "title": "Accuracy and Precision of Methods for Community Identification in   Weighted Networks",
    "url": "https://arxiv.org/abs/physics/0607271"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "6286d8dc-1a81-4912-bd3a-1076b0a065f0",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}