Systematic identification of statistically significant network measures

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dc.creatorZiv, Etay
dc.creatorKoytcheff, Robin
dc.creatorMiddendorf, Manuel
dc.creatorWiggins, Chris
dc.date2003-06-24
dc.date2005-01-27
dc.date.accessioned2026-07-07T02:52:00Z
dc.date.available2026-07-07T02:52:00Z
dc.descriptionWe present a novel graph embedding space (i.e., a set of measures on graphs) for performing statistical analyses of networks. Key improvements over existing approaches include discovery of "motif-hubs" (multiple overlapping significant subgraphs), computational efficiency relative to subgraph census, and flexibility (the method is easily generalizable to weighted and signed graphs). The embedding space is based on {\it scalars}, functionals of the adjacency matrix representing the network. {\it Scalars} are global, involving all nodes; although they can be related to subgraph enumeration, there is not a one-to-one mapping between scalars and subgraphs. Improvements in network randomization and significance testing--we learn the distribution rather than assuming gaussianity--are also presented. The resulting algorithm establishes a systematic approach to the identification of the most significant scalars and suggests machine-learning techniques for network classification.
dc.description19 pages, 12 figures
dc.identifierhttps://arxiv.org/abs/cond-mat/0306610
dc.identifierhttp://arxiv.org/abs/cond-mat/0306610
dc.identifierPhys. Rev. E 71, 016110 (2005)
dc.identifierdoi:10.1103/PhysRevE.71.016110
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/21683
dc.subjectDisordered Systems and Neural Networks
dc.subjectStatistical Mechanics
dc.subjectQuantitative Biology
dc.titleSystematic identification of statistically significant network measures
dc.typetext

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