Comput Math Methods Med - An analytical approach to network motif detection in samples of networks with pairwise different vertex labels.

Tópicos

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Resumo

Network motifs, overrepresented small local connection patterns, are assumed to act as functional meaningful building blocks of a network and, therefore, received considerable attention for being useful for understanding design principles and functioning of networks. We present an extension of the original approach to network motif detection in single, directed networks without vertex labeling to the case of a sample of directed networks with pairwise different vertex labels. A characteristic feature of this approach to network motif detection is that subnetwork counts are derived from the whole sample and the statistical tests are adjusted accordingly to assign significance to the counts. The associated computations are efficient since no simulations of random networks are involved. The motifs obtained by this approach also comprise the vertex labeling and its associated information and are characteristic of the sample. Finally, we apply this approach to describe the intricate topology of a sample of vertex-labeled networks which originate from a previous EEG study, where the processing of painful intracutaneous electrical stimuli and directed interactions within the neuromatrix of pain in patients with major depression and healthy controls was investigated. We demonstrate that the presented approach yields characteristic patterns of directed interactions while preserving their important topological information and omitting less relevant interactions.

Resumo Limpo

network motif overrepres small local connect pattern assum act function meaning build block network therefor receiv consider attent use understand design principl function network present extens origin approach network motif detect singl direct network without vertex label case sampl direct network pairwis differ vertex label characterist featur approach network motif detect subnetwork count deriv whole sampl statist test adjust accord assign signific count associ comput effici sinc simul random network involv motif obtain approach also compris vertex label associ inform characterist sampl final appli approach describ intric topolog sampl vertexlabel network origin previous eeg studi process pain intracutan electr stimuli direct interact within neuromatrix pain patient major depress healthi control investig demonstr present approach yield characterist pattern direct interact preserv import topolog inform omit less relev interact

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