Preferential attachment growth model and nonextensive statistical mechanics
Danyel J.B. Soares, Constantino Tsallis, Ananias M. Mariz, Luciano, R. da Silva

TL;DR
This paper introduces a two-dimensional growth model for networks where new sites attach based on distance and degree, revealing that the degree distribution follows a nonextensive statistical mechanics distribution and exploring the dynamics of link growth.
Contribution
The study presents a novel network growth model incorporating distance-dependent attachment probabilities and links it to nonextensive statistical mechanics, extending understanding of scale-free networks.
Findings
Degree distribution follows a q-exponential form.
Entropic index q depends on the attachment parameter α_A.
Average links grow as a power law with time, with an exponent related to α_A.
Abstract
We introduce a two-dimensional growth model where every new site is located, at a distance from the barycenter of the pre-existing graph, according to the probability law , and is attached to (only) one pre-existing site with a probability ; is the number of links of the site of the pre-existing graph, and its distance to the new site). Then we numerically determine that the probability distribution for a site to have links is asymptotically given, for all values of , by , where is the function naturally emerging within nonextensive statistical mechanics. The entropic index is numerically given (at least for not too large) by , and the characteristic number of…
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