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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.

We introduce a discrete-time distributed consensus process on multi-layered complex networks represented by multiplex graphs. The proposed consensus process can be characterized with a multiplex Markov chain (MC) composed of a mixture of interlayer and intralayer MCs. The interlayer MC is characterized by a parameter which represents the probability that the chain will switch between different layers of the multiplex network. Surprisingly, for large regions of the parameter space the convergence speed of the multiplex MC is determined only by the convergence speed of the interlayer MC. Moreover, as the number of layers in the network is increased, these regions increase until they encompass the whole parameter space. We also show that for some regions of the parameter space, the multiplex MC has a faster convergence speed than the MCs on top of the individual layers, thus complementing previous results on diffusion on multiplex networks.

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