dorsal/arxiv
View SchemaWhat do we learn from correlations of local and global network properties?
| Authors | Magnus Jungsbluth, Bernd Burghardt, Alexander K. Hartmann |
|---|---|
| Categories | |
| ArXiv ID | physics/0607150 |
| URL | https://arxiv.org/abs/physics/0607150 |
| DOI | 10.1016/j.physa.2007.03.029 |
Abstract
In complex networks a common task is to identify the most important or "central" nodes. There are several definitions, often called centrality measures, which often lead to different results. Here we study extensively correlations between four local and global measures namely the degree, the shortest-path-betweenness, the random-walk betweenness and the subgraph centrality on different random-network models like Erdos-Renyi, Small-World and Barabasi-Albert as well as on different real networks like metabolic pathways, social collaborations and computer networks. Correlations are quite different between the real networks and the model networks questioning whether the models really reflect all important properties of the real world.
{
"annotation_id": "5c68041f-e28d-4469-a6fa-5af1107878e4",
"date_created": "2026-03-02T18:01:10.905000Z",
"date_modified": "2026-03-02T18:01:10.905000Z",
"file_hash": "e659cfe2b1012cd29f77e5f00850d4a2263f8865fca4d4f24fa9159683b0b9a2",
"private": false,
"record": {
"abstract": "In complex networks a common task is to identify the most important or\n\"central\" nodes. There are several definitions, often called centrality\nmeasures, which often lead to different results. Here we study extensively\ncorrelations between four local and global measures namely the degree, the\nshortest-path-betweenness, the random-walk betweenness and the subgraph\ncentrality on different random-network models like Erdos-Renyi, Small-World and\nBarabasi-Albert as well as on different real networks like metabolic pathways,\nsocial collaborations and computer networks. Correlations are quite different\nbetween the real networks and the model networks questioning whether the models\nreally reflect all important properties of the real world.",
"arxiv_id": "physics/0607150",
"authors": [
"Magnus Jungsbluth",
"Bernd Burghardt",
"Alexander K. Hartmann"
],
"categories": [
"physics.soc-ph",
"cond-mat.dis-nn"
],
"doi": "10.1016/j.physa.2007.03.029",
"title": "What do we learn from correlations of local and global network properties?",
"url": "https://arxiv.org/abs/physics/0607150"
},
"schema_id": "dorsal/arxiv",
"source": {
"execution_id": "dd0b257b-e489-426c-b937-876931037215",
"id": "arXiv Dataset IDs",
"type": "Model",
"variant": "snapshot-2026-03-01",
"version": "0.1.0"
},
"user_id": 1000002
}