The likelihood-ratio test for multi-edge network models

Abstract

The complexity underlying real-world systems implies that standard statistical hypothesis testing methods may not be adequate for these peculiar applications. Specifically, we show that the likelihood-ratio test's null-distribution needs to be modified to accommodate the complexity found in multi-edge network data. When working with independent observations, the p-values of likelihood-ratio tests are approximated using a ${\chi }^2$ distribution. However, such an approximation should not be used when dealing with multi-edge network data. This type of data is characterized by multiple correlations and competitions that make the standard approximation unsuitable. We provide a solution to the problem by providing a better approximation of the likelihood-ratio test null-distribution through a Beta distribution. Finally, we empirically show that even for a small multi-edge network, the standard ${\chi }^2$ approximation provides erroneous results, while the proposed Beta approximation yields the correct p-value estimation.

15 pages, 3 figures

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• Cooperative Game Theory Approaches for Network Partitioning
• Maps of random walks on complex networks reveal community structure
• Stochastic blockmodels and community structure in networks
• Parsimonious module inference in large networks
• Generalized Hypergeometric Ensembles: Statistical Hypothesis Testing in Complex Networks

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