In complex networks, each node has some unique characteristics that define the importance of the node based on the given application-specific context. These characteristics can be identified using various centrality metrics defined in the literature. Some of these centrality measures can be computed using local information of the node, such as degree centrality and semi-local centrality measure. Others use global information of the network like closeness centrality, betweenness centrality, eigenvector centrality, Katz centrality, PageRank, and so on. In this survey, we discuss these centrality measures and the state of the art literature that includes the extension of centrality measures to different types of networks, methods to update centrality values in dynamic networks, methods to identify top-k nodes, approximation algorithms, open research problems related to the domain, and so on. The paper is concluded with a discussion on application specific centrality measures that will help to choose a centrality measure based on the network type and application requirements.

中文翻译：

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复杂网络中的中心性度量：一项调查

在复杂网络中，每个节点都有一些独特的特征，这些特征根据给定的特定于应用程序的上下文定义节点的重要性。可以使用文献中定义的各种中心性度量来识别这些特征。这些中心性度​​量中的一些可以使用节点的局部信息来计算，例如度中心性和半局部中心性度量。其他使用网络的全局信息，如接近中心性、中介中心性、特征向量中心性、Katz 中心性、PageRank 等。在本次调查中，我们讨论了这些中心性度​​量和最先进的文献，包括将中心性度量扩展到不同类型的网络、更新动态网络中的中心性值的方法、识别 top-k 节点的方法、近似算法、与领域相关的开放研究问题，等等。本文最后讨论了特定于应用程序的中心性度量，这将有助于根据网络类型和应用程序要求选择中心性度量。