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The eigenvector centrality determined the influence of certain developers in the social network.
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For example, in the context of learning about a new product or technology, the DeGroot (1974) model of social learning implies that nodes' eigenvector centrality determines how influential they are in influencing others' behaviour.
The largest reach centrality determines how far a node can send or receive information within the network.
The eccentricity centrality determines whether a node resides in the center of the network and whether the distribution of nodes around the central node is symmetric.
Closeness centrality was determined to act similarly to degree centrality in that "Education" has the greatest amount of degree centrality whereas "Cost of Housing" has the least.
Through betweenness centrality, we determined gatekeepers between developers.
The eigenvector centrality is determined by C_{text{E}} left( {p_{k} } right) = sumlimits_{i = 1}^{n} {a_{ki} v_{i} }. (10).
The preference of each individual vi for a certain measure of partner centrality was determined at the outset of computation and all individuals in a given network had the same preference (i.e. only a single phenotype) which remained unchanged throughout the simulation (i.e. no phenotypic plasticity).
However, the problem with network science is that there are numerous different ways of measuring centrality and determining clusters, and it is not always clear why one method should be preferred over another.
(4) Eigen-vector centrality which determines how popular a given node is.
We used this functional degree centrality to determine robustness of the network.
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