{
  "annotation_id": "c79c42bb-99f6-4b8d-b2fc-7c072e5b94a2",
  "date_created": "2026-03-02T18:01:14.158000Z",
  "date_modified": "2026-03-02T18:01:14.158000Z",
  "file_hash": "ee0e855e4b9b040571cc66f0e341005b87156ef59a0f5b38db15d06cb37374cd",
  "private": false,
  "record": {
    "abstract": "Complex networks have been applied to model numerous interactive nonlinear\nsystems in the real world. Knowledge about network topology is crucial for\nunderstanding the function, performance and evolution of complex systems. In\nthe last few years, many network metrics and models have been proposed to\nilluminate the network topology, dynamics and evolution. Since these network\nmetrics and models derive from a wide range of studies, a systematic study is\nrequired to investigate the correlations between them. The present paper\nexplores the effect of degree correlation on the other network metrics through\nstudying an ensemble of graphs where the degree sequence (set of degrees) is\nfixed. We show that to some extent, the characteristic path length, clustering\ncoefficient, modular extent and robustness of networks are directly influenced\nby the degree correlation.",
    "arxiv_id": "physics/0611078",
    "authors": [
      "Jing Zhao",
      "Lin Tao",
      "Hong Yu",
      "Jian-Hua Luo",
      "Zhi-Wei Cao",
      "Yi-Xue Li"
    ],
    "categories": [
      "physics.soc-ph",
      "physics.data-an"
    ],
    "doi": "10.1088/1009-1963/16/12/004",
    "journal_ref": "Chinese Physics 2007, 16:3571-3580",
    "title": "The effects of degree correlations on network topologies and robustness",
    "url": "https://arxiv.org/abs/physics/0611078"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "35a8377f-eda6-463a-be6e-7b4860bbb29a",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}