We address a problem of efficiently estimating value of a centrality measure for a node in a large social network only using a partial network generated by sampling nodes from the entire network. To this end, we propose a resampling-based framework to estimate the approximation error defined as 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 three real world networks, and show that it allows us to estimate the approximation error more tightly and more precisely with the confidence level of 95% even for a small partial network compared with the standard error traditionally used, and that we could potentially identify top nodes and possibly rank them in a given centrality measure with high confidence level only from a small partial network.
CITATION STYLE
Ohara, K., Saito, K., Kimura, M., & Motoda, H. (2014). Resampling-based framework for estimating node centrality of large social network. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8777, pp. 228–239). Springer Verlag. https://doi.org/10.1007/978-3-319-11812-3_20
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