{
  "annotation_id": "0cdcbd05-a70e-464d-a143-2153c6f69d87",
  "date_created": "2026-03-02T18:01:10.258000Z",
  "date_modified": "2026-03-02T18:01:10.258000Z",
  "file_hash": "47989008ff3b41c17171916a01e1854f72fa04ae331cb3aee113b10e8fd47f67",
  "private": false,
  "record": {
    "abstract": "Based on cluster de-synchronization properties of phase oscillators, we\nintroduce an efficient method for the detection and identification of modules\nin complex networks. The performance of the algorithm is tested on computer\ngenerated and real-world networks whose modular structure is already known or\nhas been studied by means of other methods. The algorithm attains a high level\nof precision, especially when the modular units are very mixed and hardly\ndetectable by the other methods, with a computational effort ${\\cal O}(KN)$ on\na generic graph with $N$ nodes and $K$ links.",
    "arxiv_id": "physics/0607179",
    "authors": [
      "S. Boccaletti",
      "M. Ivanchenko",
      "V. Latora",
      "A. Pluchino",
      "A. Rapisarda"
    ],
    "categories": [
      "physics.soc-ph",
      "cond-mat.stat-mech",
      "physics.comp-ph"
    ],
    "doi": "10.1103/PhysRevE.75.045102",
    "journal_ref": "Physical Review E 75 (2007) 045102(R)",
    "title": "Detection of Complex Networks Modularity by Dynamical Clustering",
    "url": "https://arxiv.org/abs/physics/0607179"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "84486262-7164-4063-b738-ebb4d5e36f50",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}