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The degree distribution is an important characteristic in complex networks. In many applications, quantification of degree distribution in the form of a fixed-length feature vector is a necessary step. Additionally, we often need to compare the degree distribution of two given networks and extract the amount of similarity between the two distributions. In this paper, we propose a novel method for...
From the perspective of Markov Chain, the stability of a modified extended BA model is studied in the present paper. Based on the concept and technique of the Markov chains, we give the steady-state degree distribution an explicit analysis. The approach based on Markov chain theory is universal and performs well in a large class of evolving networks.
In line with the BA model, we propose a new growing network, Group preferential model, which incorporates a precise evolving mechanism. And from the perspective of Markov chain, explicit formulas are derived analytically characterizing the evolution and distribution of degree, which is scale-free with scaling exponent related with parameter m.
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