Fig. 1.

(A) Two artificial networks generated in the hyperbolic space using the nonuniform popularity-similarity optimization (nPSO) model (8), with the following parameters: network 1 n = 100, m = 2, T = 0.1, γ = 5, and C = 10; network 2 n = 100, m = 2, T = 0.3, γ = 2.5, and C = 10. N is the network size, m is approximately half of the average node degree, T is the network temperature (inversely related to the clustering), γ is the exponent of the scale-free degree distribution, and C is the number of communities. (B) Bar plots comparing the success ratio, stretch, and GR-score for the two networks; the red arrow indicates which network has a better result for each evaluation measure. Due to the high γ, network 1 is lacking hubs that act as bridges in the navigation between nodes far from each other, and therefore it has a lower success ratio than network 2. However, the successful GR paths of network 1 have a lower stretch than the more numerous ones of network 2, since they are mostly local paths and the higher clustering (lower temperature) facilitates the navigation between nearby nodes. The GR-score provides a unique solution to the conflicting results of the other two measures, suggesting that network 2 has higher navigability, which is reasonable since the success ratio in network 2 is ∼12% higher than in network 1, whereas the stretch decrement of network 1 on network 2 is ∼2%.