Fig. 2

L3 outperforms Common Neighbors (CN) on PPI networks. Monte Carlo cross-validation of CN (a TCP implementation) and L3 on the four possible PPI data sources, arising from literature curation with multiple evidences (a, b⁵) or systematic screens (c⁵, d⁶). We randomly select 50% of the PPIs and use it as the input network to predict the rest of the PPIs. Precision is the fraction of interacting proteins vs. all predicted pairs, while recall stands for the fraction of predicted PPIs compared to the number of test PPIs. We use all predictions until a 10% recall value is reached in each network. We find that L3 outperforms CN in all cases. We find qualitatively very similar results in a k-fold cross-validation scenario, as shown in the limit of an exhaustive leave-one-out cross-validation in Supplementary Fig. 10. In addition, we show the performance of both methods on randomized networks, where only the node degrees are preserved. L3 outperforms both these random benchmarks, irrespectively of the data source. In the case of the systematic binary network, HI-II-14, CN performs worse than in the randomized network, indicating a fundamental failure of TCP to capture the patterns shaping the underlying network structure. The shading around each curve indicates the standard deviation over 10 different random selections of the input PPIs. For additional datasets and validation see Supplementary Fig. 4