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基于自相似性的复杂网络(Complex networks based on self-similarity).doc

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基于自相似性的复杂网络(Complex networks based on self-similarity) Complex networks based on self-similarity This article is contributed by yixing13 The self-similarity study of complex networks based on capacity dimensions Reporter: tao shaohua guide: professor liu yuhua in March 2006 The results of complex network model are presented in this paper 1. Introduction In 1960, the mathematician Erdos and Renyi proposed the random graph theory, and studied the random topology model in the complex network. Since then, ER model has been the basic model for studying the complex network. However, recent studies have found that many experimental data obtained in the real network are not consistent with the random graph model, so a new network model is needed to reasonably describe the actual network. In 1998, Watts and Strgatz proposed the small world (WS) model [2], which depicts the characteristics of the real network and some large clustering and short average path distance. Network has also been counted in the real world, however few contact with lots of connections, and numerous contacts with only a small amount of the characteristics of the connection, these cannot rationally explain them with random graph model. In 1999 Barabasi and Albert proposed a scale-free model (BA) [3]. The BA model points out two basic principles that determine the scale model of the Internet and the world wide web: growth and preferential connection. Although the small world network and the scale-free network depict the basic characteristics of the network, they are based on the premise of simplifying the real network. Therefore, it is necessary to delve into complex network modeling to make it more realistic. In this paper, the self-similarity of the network is proposed, and the network is formed through node and node, and the node and node are connected together through some common thread. Like the birds of a feather flock together between people. . 2. Brief introduction of complex network model A comp
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