Testing Central Structure for Complex Networks

Abstract:Centrality measures rank nodes even when the observed differences can be explained by ordinary structural variation. We therefore study a preceding question: whether a network contains a statistically identifiable central structure at all. We formulate this question as a network-level hypothesis testing issue using the adjacency spectral radius as the test statistic and estimate an empirical p-value from randomized networks generated under a specified null model. The framework is evaluated using G(N,M), Chung-Lu, and two implementations of the configuration model on simulated benchmarks and 55 real networks. Controlled experiments further vary the number and connection weights of designated central nodes while holding network size and density fixed. The results show a clear negative association between central-structure strength and the empirical p-value. They also demonstrate that rejection decisions depend substantially on the structural properties preserved by the null model: the detected network sets are not simply nested across models.

Machine Learning (cs.LG)

arXiv:2610.15659 [cs.LG]

(or arXiv:2610.15659v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.15659 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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