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However, we can extend the definition of a k-core to directed graphs by considering the in and out degree of a given graph.
In addition, the crystallinity degree of a given cellulose material has been reported to affect the OTR [5, 8], i.e. increasing the crystallinity degree reduces the oxygen permeability.
A novel rough distance is introduced to measure the dissimilarity between base partitions and the notion of knowledge granulation is improved to measure the agglomeration degree of a given granule.
This comes from the fact that the inversions simply minimize SV misfit only up to the truncation degree of a given SV model, while we consider the FF+TG condition, including all possible SV prediction at the CMB.
Thus there is no correlation between the in- and out-degrees of a given node.
The degree of a given region characterized where each connection is counted once in the unweighted connectivity matrix.
Leverage accounts not only for the degree of a given node, but also for the degree of its neighbors, thereby capturing local assortative or disassortative behavior.
Conversely, the subgroup with higher eigenvector centrality (orange) consists of nodes where the disparity between the degree of a given node and of the neighbors is less pronounced than in the green subgroup.
Leverage centrality is a measure of the relationship between the degree of a given node (ki) and the degree of each of its neighbors (kj), averaged over all neighbors (Ni), and is defined as shown below.
The connectivity degree of a given node (protein) represents the number of interactions that this node has in a particular network.
One may assume then that the value of the factor κ as calculated from Eq. 11 makes the physical characterisation of the polarity degree of a given cationomer.
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