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When centralities are categorized by their approach to cohesiveness, it becomes apparent that the majority of centralities inhabit one category. The count of the number of walks starting from a given vertex differs only in how walks are defined and counted. Restricting consideration to this group allows for a soft characterization which places centralities on a spectrum from walks of length one (degree centrality) to infinite walks (eigenvalue centrality). The observation that many centralities share this familial relationships perhaps explains the high rank correlations between these indices.

When centralities are categorized by their approach to cohesiveness, it becomes apparent that the majority of centralities inhabit one category. The count of the number of walks starting from a given vertex differs only in how walks are defined and counted. Restricting consideration to this group allows for a soft characterization which places centralities on a spectrum from walks of length one (degree centrality) to infinite walks (eigenvalue centrality). The observation that many centralities share this familial relationships perhaps explains the high rank correlations between these indices.

当中心性按照它们对'''<font color="#ff8000"> 内聚性Cohesiveness</font>'''的趋近程度来分类时，很明显，大多数中心性都集中在一个类别中。从一个给定顶点开始对步数计数，不同之处只在于行走的定义和计数方式。对该组描述的约束限制，从位置中心（度中心度）到单元域（特征中心度），观察到许多中心性都有这种相似关系，这也许可以解释这些指数之间的高阶相关性。

当中心性按照它们对'''<font color="#ff8000"> 内聚性Cohesiveness</font>'''的趋近程度来分类时，很明显，大多数中心性都集中在一个类别中。从一个给定顶点开始对步数计数，不同之处只在于行走的定义和计数方式。对该组描述的约束限制，从位置中心（'''<font color="#ff8000"> 度中心度Degree centrality</font>'''）到单元域（'''<font color="#ff8000"> 特征中心度Eigenvalue centrality</font>'''），观察到许多中心性都有这种相似关系，这也许可以解释这些指数之间的高阶相关性。

===Characterization by network flows==

===Characterization by network flows==