{
  "annotation_id": "ae438356-eb3e-4250-9a59-858317e4bb1a",
  "date_created": "2026-03-02T18:00:57.010000Z",
  "date_modified": "2026-03-02T18:00:57.010000Z",
  "file_hash": "df2a9aca7ff11ef6f0e1c687c913fbbf1029ceeaf53eb99adc48a24ad71a83f9",
  "private": false,
  "record": {
    "abstract": "We review and improve a recently introduced method for the detection of\ncommunities in complex networks. This method combines spectral properties of\nsome matrices encoding the network topology, with well known hierarchical\nclustering techniques, and the use of the modularity parameter to quantify the\ngoodness of any possible community subdivision. This provides one of the best\navailable methods for the detection of community structures in complex systems.",
    "arxiv_id": "physics/0504059",
    "authors": [
      "L. Donetti",
      "M. A. Munoz"
    ],
    "categories": [
      "physics.soc-ph",
      "cond-mat.stat-mech"
    ],
    "doi": "10.1063/1.2008598",
    "title": "Improved spectral algorithm for the detection of network communities",
    "url": "https://arxiv.org/abs/physics/0504059"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "65860dd9-e36d-43bd-84d8-01470c6828e8",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}