论文标题
复杂网络中的中心度度量：一项调查
Centrality Measures in Complex Networks: A Survey
论文作者
论文摘要
在复杂的网络中，每个节点都有一些独特的特征，可以根据给定的应用程序特定上下文定义节点的重要性。可以使用文献中定义的各种集中度指标来识别这些特征。可以使用节点的局部信息来计算其中一些中心度度量，例如学位中心性和半局部中心度度量。其他人则使用该网络的全球信息，例如紧密的中心性，中心性，特征向量中心性，Katz中心性，Pagerank等。在这项调查中，我们讨论了这些集中度度量以及艺术文献的状态，其中包括将中心度度量扩展到不同类型的网络，更新动态网络中的中心价值的方法，识别TOP-K节点，近似算法的方法，近似算法，与域相关的开放研究问题等等。本文以讨论特定的特定中心度度量的讨论来结束，这将有助于根据网络类型和应用程序要求选择中心度度量。
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.
