The likelihood-ratio test for multi-edge network models

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