更改

== Dissimilarity based centrality measures ==

== Dissimilarity based centrality measures ==

[[File:Srep17095-f1.jpg|thumbnail|In the illustrated network, green and red nodes are the most dissimilar because they do not share neighbors between them. So, the green one contributes more to the centrality of the red one than the gray ones, because the red one can access to the blue ones only through the green, and the gray nodes are redundant for the red one, because it can access directly to each gray node without any intermediary.]]

[[File:Srep17095-f1.jpg|thumbnail|In the illustrated network, green and red nodes are the most dissimilar because they do not share neighbors between them. So, the green one contributes more to the centrality of the red one than the gray ones, because the red one can access to the blue ones only through the green, and the gray nodes are redundant for the red one, because it can access directly to each gray node without any intermediary.]]

Where this measure permits us to quantify the topological contribution (which is why is called contribution centrality) of each node to the centrality of a given node, having more weight/relevance those nodes with greater dissimilarity, since these allow to the given node access to nodes that which themselves can not access directly.

Where this measure permits us to quantify the topological contribution (which is why is called contribution centrality) of each node to the centrality of a given node, having more weight/relevance those nodes with greater dissimilarity, since these allow to the given node access to nodes that which themselves can not access directly.

这种度量允许我们量化每个节点对给定节点中心性的拓扑贡献(这就是为什么我们称之为贡献中心性) ，对那些相异性较大的节点有更多的权重/相关性，因为这些允许给定的节点访问那些它们自己不能直接访问的节点。

这种度量允许我们量化每个节点对给定节点中心性的拓扑贡献(这就是为什么我们称之为贡献中心性) ，对那些相异性较大的节点有更多的权重/相关性，因为这些节点允许给定的节点访问那些它们自己不能直接访问的节点。

其中 < math > n </math > 是网络中的节点数。几个不同的措施和网络进行了测试，以获得改善的结果在研究的案例。

其中 < math > n </math > 是网络中的节点数。几个不同的措施和网络进行了测试，以获得改善的结果在研究的案例。

==Extensions==

==Extensions==