Current measures of network complexity fail to capture the structural and functional diversity of brain networks. Here we use random walks processes to obtain a time series reflecting the complex structure of functional brain networks and use this time series to construct measures of local and global complexity. We found that complexity is significantly correlated to the strength of the connections in the network. For the positively correlated network this correlation is significantly weaker at the local scale compared to the global scale, whereas for the anticorrelation network the link is stronger at the local scale.
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