Table 2 Four fitting models of degree distribution for each network

Gaussian distribution \( y={y}_0+\frac{A}{\omega \sqrt{\pi /2}}\exp \left(\frac{-2{\left(x-{x}_c\right)}^2}{\omega}\right) \)

4.26 ± 1.04

2.85 ± 1.09

4.56 ± 0.88

7.03 ± 1.72

7.80 ± 0.03

1.54 ± 0.06

−8.83 ± 10.13

−0.95 ± 1.12

4.18 ± 0.08

1.51 ± 2.91

3.36 ± 0.18

3.65 ± 2.06

7.68 ± 0.07

−5.43 ± 3.12

6.57 ± 1.12

1.02 ± 1.77

0.7652

0.2695

0.9837

0.9822

Power law distribution y = a ⋅ x ^b

6.64 ± 1.03

3.86 ± 0.035

1.29 ± 0.032

2.38 ± 0.06

0.850 ± 0.19

−1.04 ± 0.01

−1.01 ± 0.03

−1.10 ± 0.03

0.9946

0.9945

0.9628

0.9623

Log-normal distribution \( y={y}_0+\frac{A}{\omega x\sqrt{2\pi }}\exp \left(\frac{-{\left(\ln \left(x/{x}_c\right)\right)}^2}{2{\omega}^2}\right) \)

4.89 ± 1.96

3.03 ± 0.94

0.45 ± 4.21

0.84 ± 7.91

7.36 ± 0.98

1.18 ± 0.26

1.09 ± 0.72

1.09 ± 0.69

0.69 ± 0.10

0.82 ± 0.26

1.12 ± 0.69

1.09 ± 0.67

8.17 ± 3.15

5.50 ± 0.44

1.86 ± 0.17

3.45 ± 0.32

0.6469

0.7691

0.6214

0.6205

Exponential distribution y = y ₀ + A ₁ exp(x/t ₁)

6.68 ± 1.32

1.30 ± 0.15

1.55 ± 0.25

2.42 ± 5.19

9.35 ± 0.96

6.47 ± 0.39

1.58 ± 0.03

2.77 ± 0.06

6.68 ± 0.78

1.70 ± 0.11

2.96 ± 0.08

6.35 ± 0.32

0.9816

0.9368

0.9881

0.9853