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28 March 2024
 
  » pubmed » pmid15697661

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Systematic identification of statistically significant network measures
Etay Ziv ; Robin Koytcheff ; Manuel Middendorf ; Chris Wiggins ;
Date 31 Dec 2004
Journal Phys Rev E, 71 (1 Pt 2), 016110
AbstractWe present a 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 scalars, functionals of the adjacency matrix representing the network. 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.
Source PubMed, pmid15697661
Other source [GID 575863] cond-mat/0306610
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