When comparing the algorithm to the results obtained for benchmark graph theoretical algorithms the SBM-based approach continues to perform remarkably well (Figure ). The SBM-based approach clearly outperforms all tested metrics. It is worth pointing out that amongst the benchmark algorithms only the preferential attachment approach manages to exceed an AUC of 0.5, thus all other metrics fare, at times significantly, worse than using chance alone in predicting missing reactions. In a significant number of publications the reported data is unreliable, meaning the data cannot easily be used for prediction, unless rigorous statistical analysis is employed . A consequence of the lack of statistical treatment of the data by, for example, the "conventional" metrics, is that they are liable to over fitting. If the data used contains erroneous entries this is a problem when using it to predict new, unknown reactions. One of the big advantages of the SBM-based approach in comparison is its ability to trade off noise versus genuine statistical features of the data .