dorsal/arxiv
View SchemaEdge vulnerability in neural and metabolic networks
| Authors | Marcus Kaiser, Claus C. Hilgetag |
|---|---|
| Categories | |
| ArXiv ID | q-bio/0403015 |
| URL | https://arxiv.org/abs/q-bio/0403015 |
| DOI | 10.1007/s00422-004-0479-1 |
| Journal | Biological Cybernetics 90:311-317 (May 2004) |
Abstract
Biological networks, such as cellular metabolic pathways or networks of corticocortical connections in the brain, are intricately organized, yet remarkably robust toward structural damage. Whereas many studies have investigated specific aspects of robustness, such as molecular mechanisms of repair, this article focuses more generally on how local structural features in networks may give rise to their global stability. In many networks the failure of single connections may be more likely than the extinction of entire nodes, yet no analysis of edge importance (edge vulnerability) has been provided so far for biological networks. We tested several measures for identifying vulnerable edges and compared their prediction performance in biological and artificial networks. Among the tested measures, edge frequency in all shortest paths of a network yielded a particularly high correlation with vulnerability, and identified inter-cluster connections in biological but not in random and scale-free benchmark networks. We discuss different local and global network patterns and the edge vulnerability resulting from them.
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"abstract": "Biological networks, such as cellular metabolic pathways or networks of\ncorticocortical connections in the brain, are intricately organized, yet\nremarkably robust toward structural damage. Whereas many studies have\ninvestigated specific aspects of robustness, such as molecular mechanisms of\nrepair, this article focuses more generally on how local structural features in\nnetworks may give rise to their global stability. In many networks the failure\nof single connections may be more likely than the extinction of entire nodes,\nyet no analysis of edge importance (edge vulnerability) has been provided so\nfar for biological networks. We tested several measures for identifying\nvulnerable edges and compared their prediction performance in biological and\nartificial networks. Among the tested measures, edge frequency in all shortest\npaths of a network yielded a particularly high correlation with vulnerability,\nand identified inter-cluster connections in biological but not in random and\nscale-free benchmark networks. We discuss different local and global network\npatterns and the edge vulnerability resulting from them.",
"arxiv_id": "q-bio/0403015",
"authors": [
"Marcus Kaiser",
"Claus C. Hilgetag"
],
"categories": [
"q-bio.NC",
"q-bio.MN"
],
"doi": "10.1007/s00422-004-0479-1",
"journal_ref": "Biological Cybernetics 90:311-317 (May 2004)",
"title": "Edge vulnerability in neural and metabolic networks",
"url": "https://arxiv.org/abs/q-bio/0403015"
},
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