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Focus statistics for testing network centrality on uncorrelated random graphs

Published online by Cambridge University Press:  28 December 2016

TAI-CHI WANG  and
FREDERICK KIN HING PHOA
TAI-CHI WANG
Affiliation:
National Center for High-Performance Computing, Hsinchu City 300, Taiwan (e-mail: [email protected])
FREDERICK KIN HING PHOA
Affiliation:
Institute of Statistical Science, Academia Sinica, Taipei 115, Taiwan (e-mail: [email protected])
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Abstract

Network centrality has been addressed for more than 30 years; however, few studies provided statistical tests for verifying this network characteristic. By applying the idea of focus test in spatial analysis, we propose a statistical method to test the centrality of a network. We consider not only the degree of node, but also give weights on other nodes with different lengths of the shortest paths, which are called “distances” in networks. According to the density of distance based on the hidden variable model and the property of the multinomial distribution, a test statistic called “focus centrality” is provided to evaluate a network centrality. Besides the theoretical construction, we verify that the proposed method is feasible and effective in the simulation studies. Further, two empirical network data are studied as demonstrations.

Keywords

network centralityfocus centralitydensity of distancemultinomial distributionuncorrelated random graph
Type
Research Article
Information
Network Science , Volume 4 , Issue 4 , December 2016 , pp. 460 - 473
Copyright
Copyright © Cambridge University Press 2016 

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