Sentence examples for centrality that is from inspiring English sources

Two kinds of centrality can be distinguished: (1) a natural centrality that is mainly based on the location of a site in relation to its local hinterland as well as supra-regional landscape characteristics and (2) a politically controlled centrality that is caused by human efforts to assemble central functions.

Finally we show how one of these techniques for centrality can be extended to networks with both positive and negative ties to give a new centrality measure (PN centrality) that is applicable to directed valued data with both positive and negative ties.

There are also various measures of centrality that help one to determine the most important nodes in a graph for example the betweenness centrality is a measure of centrality that is the probability that a given node is on the shortest path between two uniformly randomly chosen nodes [90].

This work proposes a new centrality metric called leverage centrality that is designed to identify critical network nodes.

This added 4 measures of journal impact to our data set bringing the total number of retrieved, existing measures to 8. In [22] and [15] we describe methods to rank journals on the basis of various social network measures of centrality [13], e.g. betweenness centrality that is calculated from journal citation- and usage graphs.

Based on the set of shortest path in a graph, some other centrality indices are worth being mentioned: stress centrality, that is based on the enumeration of shortest paths; shortest-path betweenness centrality is a kind of stress centrality that accounts for the fraction of shortest paths between two nodes that contain a third node.

Although similar in concept, there are essential differences between eigenvector and leverage centrality that are discussed in this manuscript.

Conversely, if it was in fact low C-centrality that was analogous to increased fitness, individuals could use organizational rules that would hamper the emergence of high C-centrality individuals in a group (perhaps also ensuring that they themselves would not emerge as one), as was observed in our affiliation-driven networks.

This perspective poses serious concerns on the minimum and optimal set of centralities that are needed to characterize functional properties of the network nodes (e.g., proteins, genes).

Actually it is expected, as hub genes with large weights may have high degree and betweenness centralities that are considered to be frequent indicators of genes importance (Gu et al., 2012).

To the best of our knowledge, we have not come across such a computationally lightweight centrality metric that is highly correlated with betweenness centrality.