# Table 6 Re-creation of synthetic networks.

Network p D p B p C |V| |E| d ρ L $${{\boldsymbol{\rho }}}_{{{\boldsymbol{C}}}_{{\boldsymbol{D}}}}^{{\bf{2}}}$$ Network/Closest function
random 0.52 0.84 0.78 50 1007 2.00 0.40 1.59 0.11 original network
2.17 0.42 1.59 0.12 random distance
small world 0.01 0.05 0.05 50 150 11.00 0.06 4.54 0.01 original network
14.33 0.06 4.84 0.05 euclidean distance
scale free 0.37 0.08 0.05 50 144 3.00 0.06 1.53 0.25 original network
4.18 0.06 1.83 0.38 linear regression distance
forest fire 0.10 0.60 0.20 50 93 7.00 0.04 2.16 0.07 original network
7.09 0.05 2.37 0.08 euclidean distance
hierarchical 0.11 0.71 0.00 123 246 5.00 0.02 1.70 0.12 original network
6.00 0.02 2.08 0.16 degree distance
disassortative 0.28 0.37 0.00 100 510 8.00 0.05 2.85 0.11 original network
7.00 0.05 2.88 0.25 degree distance
1. Instances where the null hypothesis (samples drawn from single distribution) cannot be rejected are marked with bold font, as well as scalar network descriptors such as diameter d or density ρ that are regenerated within ±10% margin of the original value. Metrics used to compare original networks and generated by Priority Rank model are in Table 4. The best fitting distance function was selected out of all enumerated in Table 3.