Figure 5

Likelihood-ratio test using eigenvector centrality. This statistical adjustment strategy is based on the eigenvector centrality of the candidate proteins. For the given sample network, seed proteins are represented by blue nodes and the intensity of the color of the candidates is proportional to their scores computed via different methods. In (i), two candidates are scored based on their proximity to seed proteins, calculated using random walk with restarts. In (ii), candidate proteins are scored based on their eigenvalue centrality in the network (without using any seed information). Finally in (iii), scores are assigned to candidates using the log-likelihood ratio of the values computed in (i) and (ii). Although the highly connected candidate (in the center of the network) is scored higher than the loosely connected candidate in (i) and (ii), the log-likelihood ratio of both candidates is similar as illustrated in (iii) since the association scores are adjusted by the centrality of the nodes in the network.