{
  "annotation_id": "858cb744-001b-4fc7-80e2-8b212708fd2d",
  "date_created": "2026-03-02T18:01:03.482000Z",
  "date_modified": "2026-03-02T18:01:03.482000Z",
  "file_hash": "ae06b1807141797b87b358dbfb18400c53e0828b1e870bc7b5d81a6e96e0d9cf",
  "private": false,
  "record": {
    "abstract": "We consider methods for quantifying the similarity of vertices in networks.\nWe propose a measure of similarity based on the concept that two vertices are\nsimilar if their immediate neighbors in the network are themselves similar.\nThis leads to a self-consistent matrix formulation of similarity that can be\nevaluated iteratively using only a knowledge of the adjacency matrix of the\nnetwork. We test our similarity measure on computer-generated networks for\nwhich the expected results are known, and on a number of real-world networks.",
    "arxiv_id": "physics/0510143",
    "authors": [
      "E. A. Leicht",
      "Petter Holme",
      "M. E. J. Newman"
    ],
    "categories": [
      "physics.soc-ph",
      "cond-mat.dis-nn",
      "physics.data-an"
    ],
    "doi": "10.1103/PhysRevE.73.026120",
    "journal_ref": "Phys. Rev. E 73, 026120 (2006)",
    "title": "Vertex similarity in networks",
    "url": "https://arxiv.org/abs/physics/0510143"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "c9c77cc9-2bd0-42d1-a1c8-bd1329bbe4e3",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}