Resampling-Based Framework for Unbiased Estimator of Node Centrality over Large Complex Network
Resampling-Based Framework for Unbiased Estimator of Node Centrality over Large Complex Network
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DOI：
10.1007/978-3-030-33778-0_32
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发表时间：
2019-10
期刊：
影响因子：
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通讯作者：
Kazumi Saito;K. Ohara;M. Kimura;H. Motoda
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文献类型：
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作者：
Kazumi Saito;K. Ohara;M. Kimura;H. Motoda
针对大型网络中节点中心性度量的有效估计问题，提出了一种基于抽样的框架，该框架只使用随机选择的少量节点来估计中心性度量。我们推导的误差估计器是对逼近误差的无偏估计，其定义为中心性的真值和估计值之间的差的期望。我们在六个不同领域的真实世界网络上使用贴近度和介数中心度对该框架的基本性能进行了实验评估，结果表明，与传统的基于I.I.D.的标准误差估计器相比，它使我们能够更紧密和更精确地估计逼近误差。抽样，即对于少量抽样，具有置信度的抽样，表示节点总数。
We address a problem of efficiently estimating value of a centrality measure for a node in a large network, and propose a sampling-based framework in which only a small number of nodes that are randomly selected are used to estimate the measure. The error estimator we derived is an unbiased estimator of the approximation error defined as the expectation of the difference between the true and the estimated values of the centrality. We experimentally evaluate the fundamental performance of the proposed framework using the closeness and betweenness centralities on six real world networks from different domains, and show that it allows us to estimate the approximation error more tightly and more precisely than the standard error estimator traditionally used based on i.i.d. sampling,i.e., with the confidence level offor a small number of sampling, sayof the total number of nodes.