{
  "annotation_id": "61c40b16-7af9-407b-bc46-9e88e1cfb3b8",
  "date_created": "2026-03-02T18:01:18.194000Z",
  "date_modified": "2026-03-02T18:01:18.194000Z",
  "file_hash": "25a74c2098d22b78c75a21f8a1273f51f220a345bae4511ab36f862821dc423c",
  "private": false,
  "record": {
    "abstract": "We study community structure of networks. We have developed a scheme for\nmaximizing the modularity Q based on mean field methods. Further, we have\ndefined a simple family of random networks with community structure; we\nunderstand the behavior of these networks analytically. Using these networks,\nwe show how the mean field methods display better performance than previously\nknown deterministic methods for optimization of Q.",
    "arxiv_id": "physics/0701348",
    "authors": [
      "Sune Lehmann",
      "Lars Kai Hansen"
    ],
    "categories": [
      "physics.data-an"
    ],
    "doi": "10.1140/epjb/e2007-00313-2",
    "title": "Deterministic Modularity Optimization",
    "url": "https://arxiv.org/abs/physics/0701348"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "5fd4eb0f-4b48-473e-a7e7-74e97149511d",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}