{
  "annotation_id": "857b6ae8-dceb-43bf-8868-826a3838eae9",
  "date_created": "2026-03-02T18:00:42.274000Z",
  "date_modified": "2026-03-02T18:00:42.274000Z",
  "file_hash": "086b5b9c0120ddd18a54f46f0e0780c371a0f00314ebcd9fe4e87a4afeec32de",
  "private": false,
  "record": {
    "abstract": "Given a complex biological or social network, how many clusters should it be\ndecomposed into? We define the distance $d_{i,j}$ from node $i$ to node $j$ as\nthe average number of steps a Brownian particle takes to reach $j$ from $i$.\nNode $j$ is a global attractor of $i$ if $d_{i,j}\\leq d_{i,k}$ for any $k$ of\nthe graph; it is a local attractor of $i$, if $j\\in E_i$ (the set of\nnearest-neighbors of $i$) and $d_{i,j}\\leq d_{i,l}$ for any $l\\in E_i$. Based\non the intuition that each node should have a high probability to be in the\nsame community as its global (local) attractor on the global (local) scale, we\npresent a simple method to uncover a network\u0027s community structure. This method\nis applied to several real networks and some discussion on its possible\nextensions is made.",
    "arxiv_id": "physics/0302030",
    "authors": [
      "Haijun Zhou"
    ],
    "categories": [
      "physics.bio-ph"
    ],
    "doi": "10.1103/PhysRevE.67.041908",
    "journal_ref": "Physical Review E 67: 041908 (2003)",
    "title": "Network Landscape from a Brownian Particle\u0027s Perspective",
    "url": "https://arxiv.org/abs/physics/0302030"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "37ea10bd-f72f-4aba-b51e-5e535d28f254",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}