Figure 4
How robust is PageRank Affinity to noise? We add noise to a network represented by a graph G = (V, E) by randomly choosing r1 * |E| of the true positive interactions and removing them, and randomly choosing r2 * |E| of the true negative interactions and adding them. We calculate the robustness score of PageRank Affinity by computing the PageRank Affinity of each pair of vertices in the noisy network, and counting how often true positive interactions score higher than true negative interactions. As a base for comparison, we also compute the robustness score of shortest path closeness (with ties in distance broken randomly). (a) The results for r1 = 0.1 and varying values of r2. (b) The results for r1 = 0.2 and varying values of r2. (c) The results for r1 = 0.3 and varying values of r2. (d) The results for r1 = 0.4 and varying values of r2.