We address this work to investigate symbolic sequences with long-range correlations by using computational simulation. We analyze sequences with two, three and four symbols that could be repeated l times, with the probability distribution p(l) ∝ 1/l µ. For these sequences, we verified that the usual entropy increases more slowly when the symbols are correlated and the Tsallis entropy exhibits, for a suitable choice of q, a linear behavior. We also study the chain as a random walk-like process and observe a nonusual diffusive behavior depending on the values of the parameter µ.

In this work we analyze the implications of using a power law distribution of vertice's quality in the growth dynamics of a network studied by Bianconi and Barabási. Using this suggested distribution we show the degree distribution interpolates the Barabási et al. model and Bianconi et al. model. This modified model (with power law distribution) can help us understand the evolution of complex systems. Additionally, we determine the exponent gamma related to the degree distribution, the time evolution of the average number of links,< ki >∝ (t/i)β (i coincindes with the input-time of the i th node), the average path length and the clustering coefficient.