在腦網絡分析中，用到最多的方法是圖論（graph theory），其目的就是找到其重要作用的腦區，其重要性也可以了解其他腦區的作用。centrality的概念旨在通過總結節點之間的結構關係量化圖論理念，它可以實現以下幾個方面：（1）A measure of how network structure and position contributes to a node’s importance；（2）Value associated with every node；（3）Many different measures which capture different aspects；（4）Can be characterized by the nature of the flow。它包括Degree、Closeness、Betweenness和Eigenvector四個測量維度，下面對這四個維度分別介紹。
Degree Centrality
An actor with high degree centrality maintains numerous contacts with other network actors.
Actor-level degree centrality is simply each actor’s number of degrees in a undirected graph:
When ties are directed, we calculate total number of ties sent (out-degree) and ties received (in-degree). Out-degree typically indicates influence, in-degree indicates prestige or popularity.
Closeness Centrality: emphasizes the distance of an actor to all others in the network
An actor that is close to many others can quickly interact and communicate with them without going through many intermediaries.
Actor closeness centrality is the inverse of the sum of geodesic distances from actor i to the g-1 other actors
Betweenness Centrality: is the extent to which an actor falls on the geodesic paths between other pairs of actors in the network.
Actor betweenness centrality for actor i is the sum of the proportions, for all pairs of actors j and k, in which actor i is involved in a pair’s geodesic(s)
Eigenvector Centrality: a centrality measure where node’s degree is weighted by the centrality of the nodes it is connected to.
本文部分內容來自清華大學鄭路教授的講課內容，在此表示感謝！
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