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Edge vulnerability in neural and metabolic networks

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Edge vulnerability in neural and metabolic networks. / Kaiser, Marcus; Hilgetag, Claus C.

in: BIOL CYBERN, Jahrgang 90, Nr. 5, 05.2004, S. 311-7.

Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung

Harvard

Kaiser, M & Hilgetag, CC 2004, 'Edge vulnerability in neural and metabolic networks', BIOL CYBERN, Jg. 90, Nr. 5, S. 311-7. https://doi.org/10.1007/s00422-004-0479-1

APA

Kaiser, M., & Hilgetag, C. C. (2004). Edge vulnerability in neural and metabolic networks. BIOL CYBERN, 90(5), 311-7. https://doi.org/10.1007/s00422-004-0479-1

Vancouver

Kaiser M, Hilgetag CC. Edge vulnerability in neural and metabolic networks. BIOL CYBERN. 2004 Mai;90(5):311-7. https://doi.org/10.1007/s00422-004-0479-1

Bibtex

@article{fb637dd6792a4efe889c42410bd9bc59,
title = "Edge vulnerability in neural and metabolic networks",
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 intercluster 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.",
keywords = "Animals, Cats, Macaca, Models, Neurological, Nerve Net, Letter, Research Support, Non-U.S. Gov't",
author = "Marcus Kaiser and Hilgetag, {Claus C}",
year = "2004",
month = may,
doi = "10.1007/s00422-004-0479-1",
language = "English",
volume = "90",
pages = "311--7",
journal = "BIOL CYBERN",
issn = "0340-1200",
publisher = "Springer",
number = "5",

}

RIS

TY - JOUR

T1 - Edge vulnerability in neural and metabolic networks

AU - Kaiser, Marcus

AU - Hilgetag, Claus C

PY - 2004/5

Y1 - 2004/5

N2 - 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 intercluster 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.

AB - 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 intercluster 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.

KW - Animals

KW - Cats

KW - Macaca

KW - Models, Neurological

KW - Nerve Net

KW - Letter

KW - Research Support, Non-U.S. Gov't

U2 - 10.1007/s00422-004-0479-1

DO - 10.1007/s00422-004-0479-1

M3 - SCORING: Journal article

C2 - 15221391

VL - 90

SP - 311

EP - 317

JO - BIOL CYBERN

JF - BIOL CYBERN

SN - 0340-1200

IS - 5

ER -