Open AccessEvolving Networks with Enhanced Stability Properties
Author(s) -
David Newth,
Jeff Ash
Publication year - 2008
Publication title -
physics research international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.136
H-Index - 13
eISSN - 2090-2239
pISSN - 2090-2220
DOI - 10.1155/2008/195873
Subject(s) - assortativity , instability , degree distribution , stability (learning theory) , cluster analysis , complex network , computer science , clustering coefficient , average path length , degree (music) , topology (electrical circuits) , mathematics , statistical physics , theoretical computer science , shortest path problem , artificial intelligence , physics , combinatorics , machine learning , graph , world wide web , mechanics , acoustics
We use a search algorithm to identify networks with enhanced linearstability properties in this account. We then analyze these networks fortopological regularities that explain the source of their stability/instability.Analysis of the structure of networks with enhanced stability propertiesreveals that these networks are characterized by a highly skewed degreedistribution, very short path-length between nodes, little or no clustering,and dissasortativity. By contrast, networks with enhanced instabilityproperties have a peaked degree distribution with a small variance, longpath-lengths between nodes, a high degree of clustering, and high assortativity.We then test the topological stability of these networks and discoverthat networks with enhanced stability properties are highly robust to therandom removal of nodes, but highly fragile to targeted attacks, while networkswith enhanced instability properties are robust to targeted attacks.These network features have implications for the physical and biologicalnetworks that surround us
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