Complex network building method

Abstract

The invention relates to the field of electric digital data processing, in particular to a complex network building method based on an adjacent matrix Kronecker sum, and is especially suitable for data processing methods with specific functions. The complex network building method includes that on the basis of a simple generating network, Kronecker sum operation is performed on adjacent matrixes of the simple generating network iteratively to generate a complex network with degree distribution having polynomial distribution characteristics, the degree distribution of the complex network can be strictly calculated theoretically by adopting operation with coefficient multiplied and number of times added of a usual polynomial multiplication on a degree distribution expression form of the generated network. When number of iterations is large, the degree distribution of the complex network is approximate to normal distribution, has the characteristics of a classical random network and can be considered as a random network. When the generated network is simple, the degree distribution of the complex network can be acquired through existing tools or by directly adopting a polynomial expansion form. The complex network building method is especially suitable for complex network building.

Description

technical field [0001] The invention belongs to the field of electrical digital data processing, and is particularly suitable for a data processing method with a specific function, and in particular relates to a complex network construction method based on an adjacency matrix Kronecker sum. Background technique [0002] The deepening of complex network research has promoted the rise of network science, and the research objects in many fields such as natural science and social science can be abstracted into complex networks for research. The construction of complex network models occupies an extremely important position in the analysis and research of social networks, computer networks, virtual social networks and other fields. In the middle of the last century, the ER stochastic network model was proposed, which pioneered the systematic study of complex networks and became the basis for the follow-up research on complex networks for nearly half a century; at the end of the l...

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Problems solved by technology

[0011] Generally speaking, the research on the characteristics of complex networks is still a hot spot in complex network research today. It is undeniable that although there are relatively mature theories on random networks, small-world networks, scale-free networks and self-similar networks, etc. And methods, most of the research is also consistent with the real complex network, but still can not fully reflect the various characteristics of the real complex network in real life, such as the self-similar complex network model is based on a definite generation network, iteratively through its adjacency matrix Kronecker The product constructs a complex network, but does not make full use of the characteristics of its adjacency matrix, and its application range is relatively narrow. It is necessary to further study the characteristics of complex networks. The study of complex networks from the adjacency matrix of simple networks should attract sufficient attention.

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