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Resampling-based framework for estimating node centrality of large social network


Ohara, K and Saito, K and Kimura, M and Motoda, H, Resampling-based framework for estimating node centrality of large social network, Lecture Notes in Artificial Intelligence 8777: Proceedings of the 17th International Conference on Discovery Science, 8-10 October 2014, Bled, Slovenia, pp. 228-239. ISBN 978-3-319-11811-6 (2014) [Refereed Conference Paper]

Copyright Statement

Copyright 2014 Springer

DOI: doi:10.1007/978-3-319-11812-3_20


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.

Item Details

Item Type:Refereed Conference Paper
Keywords:error estimation, resampling, node centrality, social network analysis
Research Division:Information and Computing Sciences
Research Group:Distributed computing and systems software
Research Field:Networking and communications
Objective Division:Information and Communication Services
Objective Group:Information services
Objective Field:Information services not elsewhere classified
UTAS Author:Motoda, H (Dr Hiroshi Motoda)
ID Code:98916
Year Published:2014
Deposited By:Information and Communication Technology
Deposited On:2015-03-06
Last Modified:2018-01-16

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