Nonextensive Statistical Mechanics

7.8 Scale-Free Networks 285a1b1cumulative distribution P( > )cln q P( > )ecumulative distribution P( > )g0.10.010.0011 10 100 1000 10000 100000sparseness time interval [ms]0–1–2–3–4–5–6–7–80 5000 10000 15000 20000sparseness time interval [ms]10.10.010.0010.00011 10 100 1000 10000 100000sparseness time interval [ ms ]0cumulative distribution P( > )dln q P( > )fcumulative distribution P( > )h0.10.010.0010.00010–2–4–6–8–10–121 10 100 1000 10000sparseness time interval [ ms ]–140 1000 2000 3000 4000 5000sparseness time interval [ms]10.10.011 10 100 1000 10000 100000sparseness time interval [ms]0ln q P( > )–2–4–6–8–10–12–140 2000 4000 6000 8000 10000 12000sparseness time interval [ms]ln q P( > )–0.5–1–1.5–2–2.5–30 10000 20000 30000 40000 50000 60000sparseness time interval [ms]Fig. 7.87 Cumulative probability of the measured sparseness time interval corresponding to fourdifferent nonequilibrium stationary states (the first three are precisely the states a, b, and c ofFig. 7.86; the fourth is still a different one. All four upper panels are in log–log representation; allfour lower panels are the same data, in q-log vs. linear representation. The continuous curves areq-exponentials with q = 1.7 (aandc),q = 1.12 (b and d), q = 1.16 (e and g), and q = 0.73 (fand h), respectively. Notice that values of q both above and below unity occur (from [773]).