Large sparse complex network topology analysis and simplification method based on polygon coefficients

Abstract

The invention discloses a large sparse complex network topology analysis and simplification method based on a polygon coefficient. The method comprises the following steps: 1) defining a network polygon coefficient; 2) calculating a network polygon coefficient; 3) calculating a network matching coefficient; (4) performing network topology analysis; and (5) performing network topology simplification. The method is used for analyzing and simplifying the topological structure of the large-scale sparse wide area network; time consumption of a large number of traversal algorithms, heuristic algorithms and approximation algorithms based on the network topology is reduced, and the number of nodes and the number of links contained in the network can be reduced on the basis of not changing the basic architecture of the original topology.

Description

technical field [0001] The invention relates to analysis and simplification technology of network topology, in particular to a large-scale sparse complex network topology analysis and simplification method based on polygonal coefficients. Background technique [0002] With the development of 5G and Internet of Things technologies, communication and computer networks are connecting more and more terminals with different levels of intelligence to the network and providing them with services and services of different quality of service. In order to meet the service quality requirements of different types of businesses and services, the current communication and computer networks need to manage and optimize network resources flexibly and intelligently. [0003] This kind of flexible and intelligent network management and optimization is usually realized through various traversal, heuristic, learning and approximate algorithms based on network topology. The basic idea is to find...

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Problems solved by technology

For example, a network with a positive assortative coefficient indicates that nodes with similar degrees in the network are usually connected to each other; a network with a negative asassortative coefficient usually indicates that nodes with very different degrees in the network are connected together, that is, positive assortative A network with a negative assortative coefficient may have a linear or circular network topology, and a network with a negative assortative coefficient may have a star network topology, however, the difference in network topology remains unrecognizable when two networks have assortative coefficients of the same sign

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