| \(\mu\) | Precision | Recall | F1 |
---|
High precision | 0.5 | 0.78 | 0.32 | 0.45 |
High recall | 0.75 | 0.27 | 0.64 | 0.38 |
- We show how our model performs in terms of identifying anomalies—edges that have been injected in the dataset—as we vary the prior on Z, tuned by the parameter \(\mu\). Here \(\pi =0.25\) and \(\rho _{a} = 0.087\)