Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\27830769
.jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117 Sci+Rep
2016 ; 6
(ä): 37057
Nephropedia Template TP
gab.com Text
Twit Text FOAVip
Twit Text #
English Wikipedia
Graphlet characteristics in directed networks
#MMPMID27830769
Trpevski I
; Dimitrova T
; Boshkovski T
; Stikov N
; Kocarev L
Sci Rep
2016[Nov]; 6
(ä): 37057
PMID27830769
show ga
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
with Granger causality) patterns (G-causes and G-effects) than inhibitory
effective brain networks.
free
free
free
