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It is well known that the resistance distance between two arbitrary vertices in an electrical network can be obtained in terms of the eigenvalues and eigenvectors of the combinatorial Laplacian matrix and the normalized Laplacian matrix associated with the network.
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Thus, the high value of betweenness (in the range of hundreds), and the low value of its normalized counterpart (close to zero) in Knowledge-Net, indicates that the graph is either dense or is coupled in a way that there is a large number of shortest paths between any two arbitrary vertices.
Especially, all the weights in Fig. 1b are normalized into the range [0, 1], and the smaller weight of two arbitrary vertices in Fig. 1b indicates the lower collaborative cost and the better friendship between the corresponding crowd workers.
It means between two arbitrary points.
The transformations between two arbitrary tags in the calibration target.
The degree of symmetrical inversions between two arbitrary genomes with this particular genetic distance varies tremendously.
The difference between those two arbitrary extremes is measurement.
Note that, unlike standard digraphs, there can be a journey between two vertices in the SCC that traverses vertices outside (U_{mathcal{G}}).
The traditional min-cut problem involves finding a cut with minimum weight between two specified vertices.
For simplicity, the normalized vector between two successive vertices was named as the pipeline vector.
Finally, the center of the rotated pipeline was translated to the midpoint between two successive vertices.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com