Slef-Similarity Research Of Complex Networks

From the late 20th century, rapid development of the Internet information technology, marking the human society entered the Internet Age. The human society is increasingly networked, which needs of an its network structure, the commonalities between the various networks, self-similarity of understanding and dealing with their universal method, this paper has studied the self-similarity of complex networks.Of a system of self-similarity refers to the characteristics of a structure or process from a different spatial scales or time scales are similar terms, or a system or structure of local nature or a similar local structure and overall. In this paper, introduction of the self-similarity of complex networks, studied the network dimension, size distribution, and the cumulative degree.This article first introduces the basic concepts of complex networks, such as the network's basic characteristics:degree and degree distribution, the cumulative degree, the average path length, clustering coefficient; as well as the basic model of complex networks has done a brief description. Subsequently, studied the complex network of self-similarity, and introduced the fractal theory for the network dimensions. In fractal theory, its dimension calculated a variety of methods, such as fractal dimension, capacity dimension, information dimension, dimension, and the box dimension.Finally, pairs of computer-generated networks, BBS network for users interested in empirical analysis, the results showed that:These networks have self-similarity. BBS network of user interest for empirical research shows that the network is a scale-free networks, degree distribution, the community size distribution are subject to power-law distribution.