擅长:python、mysql、java
<p>这个答案来自于一个关于这个问题的Google小组(在使用R的上下文中),它有助于澄清与上述答案相关的数学问题:</p>
<blockquote>
<p>Freeman's approach measures "the average difference in centrality
between the most central actor and all others". </p>
<p>This 'centralization' is exactly captured in the mathematical formula</p>
<p>sum(max(x)-x)/(length(x)-1) </p>
<p>x refers to any centrality measure! That is, if you want to calculate
the degree centralization of a network, x has simply to capture the
vector of all degree values in the network. To compare various
centralization measures, it is best to use standardized centrality
measures, i.e. the centrality values should always be smaller than 1
(best position in any possible network) and greater than 0 (worst
position)... if you do so, the centralization will also be in the
range of [0,1]. </p>
<p>For degree, e.g., the 'best position' is to have an edge to all other
nodes (i.e. incident edges = number of nodes minus 1) and the 'worst
position' is to have no incident edge at all.</p>
</blockquote>