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Graphlet Characteristics in Directed Networks by Igor Trpevski; Tamara Dimitrova; Tommy Boshkovski; Nikola Stikov; Ljupcho Kocarev is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

What is Graphlet Characteristics in Directed Networks about?

## Abstract Graphlet analysis is part of network theory that does not depend on the choice of the network null model and can provide comprehensive description of the local network structure. Here, we propose a novel method for graphlet-based analysis of directed networks by computing first the __signature vector__ for every vertex in the network and then the __graphlet correlation matrix__ of the network. This analysis has been applied to brain effective connectivity networks by considering both direction and sign (inhibitory or excitatory) of the underlying directed (effective) connectivity. In particular, the signature vectors for brain regions and the graphlet correlation matrices of the brain effective network are computed for 40 healthy subjects and common dependencies are revealed. We found that the signature vectors (node, wedge, and triangle degrees) are dominant for the excitatory effective brain networks. Moreover, by considering only those correlations (or anti correlations) in the correlation matrix that are significant (>0.7 or <−0.7) and are presented in more than 60% of the subjects, we found that excitatory effective brain networks show stronger causal (measured wit

Who reads Graphlet Characteristics in Directed Networks?

Author

Igor Trpevski; Tamara Dimitrova; Tommy Boshkovski; Nikola Stikov; Ljupcho Kocarev

Publisher

Springer Science and Business Media LLC

Published

2016

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)