Sentence examples for centrality does not from inspiring English sources

It was found that smoking seems to decrease centrality, however centrality does not cause one to decrease smoking.

It is important to note that in and out-degree centrality does not necessarily measure primary versus secondary occupations, but rather relative rankings.

Furthermore, leverage centrality does not assume information flows along the shortest path or in a serial fashion, but rather focuses on the disparity in node degrees in a small neighborhood to quantify consolidation and dissemination of information locally.

However, eigenvector centrality does not account for the disparity in the degree of a node with respect to its neighbors, which has different implications depending on the network's assortativity, or the tendency for nodes to be connected to similar degree nodes.

This means that gene centrality does not constrain the evolution of proximal promoters, unlike the case with coding regions, and further indicates that the evolution of proximal promoters is more efficient in the center of the protein network than in the periphery.

Moreover, our results suggest that closeness centrality does not consistently reflect the topological importance of house-keeping genes in PPI networks.

Although degree centrality did not relate significantly to changes in team conflict over time, a strong inverse relationship was found between changes in team conflict and cohesion, suggesting that team conflict emphasizes a different but related aspect of how individuals view their experience with the team process.

For example, the tendency to do something less important than fishing declined significantly with development, whereas the tendency to do fishing as a less important activity (as reflected by the in-degree centrality) did not significantly change with development.

The combination of more than 2 centralities did not improve the correct prediction of metabolic essential genes (see Figure 2).

While an overall correlation between the centrality measures is evident (correlation coefficients and associated p-values are provided in the legend of Table 4), each centrality measure identifies particular aspects of centrality and does not correspond directly to the robustness measure used here.

Eigenvector centrality is computationally much more efficient than betweenness centrality and does not require thresholding of similarity values so that it can be applied to thousands of voxels in a region of interest covering the entire cerebrum which would have been infeasible using betweenness centrality.