Figure 4 | Scientific Reports

Figure 4

From: A detailed characterization of complex networks using Information Theory

Figure 4

(a) Shows the results for Barabási-Albert networks using a non-linear preferential attachment with N = 1000 and \(\alpha \in [0,3]\). For the sake of visualization, we plot the red downward triangles representing \({G}^{WS}\) with β = 0, i.e., k-ring graphs; and blue upward triangles representing \({G}^{WS}\) with β = 1, i.e., random graphs. (b) Shows how changing \(\alpha \) causes disturbances in the Fisher Information Measure, when evaluating the Barabási-Albert model with non-linear PA.

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