Estimating Tsallis q for degree distributions
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[edit] Hurwicz function
http://en.wikipedia.org/wiki/Hurwitz_zeta_function goes with new estimation methods
[edit] In general
if (1 + (1 - q)x) > 0); otherwise
, where
and
if (1 + (1 - Qc)x) > 0); otherwise
, where Qc is for a cumulative distribution.
While Nonextensive aspects of self-organized scale-free gas-like networks Stefan Thurner and Constantino Tsallis give:
for
, where Qc is for a cumulative distribution.
And if for
. then for K (cumulative degree distribution for k) we might use the estimator
The upper part of these distributions tends to follow the semi-q-log straight line characteristic of the q-exponential, with steeper slopes in the tails.
is defined as (K=cum freq for each k) / cum freq for all k. Then the expectation is that:
with lnQc(k) adjusted for
or
Qc = 1 / (2 − q),q < 2
q = 2 − 1 / Qc
[edit] Method for Generative Feedback Model ("social circles") simulated networks
We begin with the model of WKTFW2006:3 (equation 4) for degree distributions with the probability density function:
Then, since
if (1 + (1 - q)x) > 0); otherwise
, where
,
An alternative distribution is given in TKT2007(5-6, equations 13-14):
[edit] Cumulative CDF
Tsallis q distribution project Tambayong, Clauset, Shalizi, White
[edit] Links
http://en.wikipedia.org/wiki/Probability_distribution#Discrete_distributions
[edit] References
WKTFW2006 White, Douglas R., Nataša Kejžar, Constantino Tsallis, Doyne Farmer, and Scott White. Physical Review E, 016119 11pp. http://arxiv.org/abs/cond-mat/0508028 http://tinyurl.com/ylpbn3 http://en.wikipedia.org/wiki/Social-circles_network_model
TKT2007 Unified model for network dynamics exhibiting nonextensive statistics Stefan Thurner, Fragiskos Kyriakopoulos, Constantino Tsallis published as Phys. Rev. E 76, 036111 (2007) (8 pages)
STMS2005 Preferential attachment growth model and nonextensive statistical mechanics Europhys. Lett., 70 (1), p. 70 (2005) D. J. B. Soares, C. Tsallis, A. M. Mariz, L. R. da Silva.

is defined as (K=cum freq for each k) / cum freq for all k. Then the expectation is that:

