{
  "annotation_id": "8ac469d4-3a28-49e8-9c58-4fa649f71351",
  "date_created": "2026-03-02T18:01:14.978000Z",
  "date_modified": "2026-03-02T18:01:14.978000Z",
  "file_hash": "ba8b24200240f4e82ada9ccde7b3bb3ce085d90f7326a04dd4b251a36d1da746",
  "private": false,
  "record": {
    "abstract": "To understand the structure of a large-scale biological, social, or\ntechnological network, it can be helpful to decompose the network into smaller\nsubunits or modules. In this article, we develop an information-theoretic\nfoundation for the concept of modularity in networks. We identify the modules\nof which the network is composed by finding an optimal compression of its\ntopology, capitalizing on regularities in its structure. We explain the\nadvantages of this approach and illustrate them by partitioning a number of\nreal-world and model networks.",
    "arxiv_id": "physics/0612035",
    "authors": [
      "Martin Rosvall",
      "Carl T. Bergstrom"
    ],
    "categories": [
      "physics.soc-ph",
      "physics.data-an"
    ],
    "doi": "10.1073/pnas.0611034104",
    "journal_ref": "PNAS 104, 7327-7331 (2007)",
    "title": "An information-theoretic framework for resolving community structure in   complex networks",
    "url": "https://arxiv.org/abs/physics/0612035"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "e26b5f4c-3bbe-424e-878c-ac8df55a3bef",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}