{
  "annotation_id": "2b7b8c16-c0c7-41c3-adc3-871352f195fd",
  "date_created": "2026-03-02T18:01:14.003000Z",
  "date_modified": "2026-03-02T18:01:14.003000Z",
  "file_hash": "b1097526385f382db63d8cc24cb9b5d23eb4c43cf262a318a6c54a8a5bb1f67f",
  "private": false,
  "record": {
    "abstract": "To account for strong aging characteristics of citation networks, we modify\nGoogle\u0027s PageRank algorithm by initially distributing random surfers\nexponentially with age, in favor of more recent publications. The output of\nthis algorithm, which we call CiteRank, is interpreted as approximate traffic\nto individual publications in a simple model of how researchers find new\ninformation. We develop an analytical understanding of traffic flow in terms of\nan RPA-like model and optimize parameters of our algorithm to achieve the best\nperformance. The results are compared for two rather different citation\nnetworks: all American Physical Society publications and the set of high-energy\nphysics theory (hep-th) preprints. Despite major differences between these two\nnetworks, we find that their optimal parameters for the CiteRank algorithm are\nremarkably similar.",
    "arxiv_id": "physics/0612122",
    "authors": [
      "Dylan Walker",
      "Huafeng Xie",
      "Koon-Kiu Yan",
      "Sergei Maslov"
    ],
    "categories": [
      "physics.soc-ph",
      "cs.DL",
      "physics.data-an"
    ],
    "doi": "10.1088/1742-5468/2007/06/P06010",
    "journal_ref": "J.Stat.Mech.0706:P06010,2007",
    "title": "Ranking Scientific Publications Using a Simple Model of Network Traffic",
    "url": "https://arxiv.org/abs/physics/0612122"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "5bb0a9ab-d15b-47a7-b72f-05935d347bc6",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}