Abstract
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 of \(95\%\) for a small number of sampling, say \(20\%\) of the total number of nodes.
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This material is based upon work supported by JSPS Grant-in-Aid for Scientific Research (C) (No. 17K00314).
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Saito, K., Ohara, K., Kimura, M., Motoda, H. (2019). Resampling-Based Framework for Unbiased Estimator of Node Centrality over Large Complex Network. In: Kralj Novak, P., Šmuc, T., Džeroski, S. (eds) Discovery Science. DS 2019. Lecture Notes in Computer Science(), vol 11828. Springer, Cham. https://doi.org/10.1007/978-3-030-33778-0_32
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