Discovery method of complex network community

Abstract

The invention discloses a discovery method of a complex network community. The discovery method comprises the following steps of: 1, establishing adjacency matrix representation of a network which is required to be analyzed; 2, determining the value of an initial partition point; 3, calculating the degree of each node in the network; 4, selecting K points with the highest node degrees as initial partition points; 5, selecting a specific needed partition point; and 6, giving a last community discovery result by using a computer according to the last partition point obtained in the step 5. The invention provides a partition-point-based discovery method for processing a complex network community for the computer. Through the method, the technical problem that a large-scale complex network cannot be processed by using the computer in the prior art is solved, a large amount of computer storage space can be saved in a calculation process, system resource occupied by calculation is reduced, and operation speed and efficiency of the computer are improved; simultaneously, a downward decomposition method and an upward polymerization method are unified.

Description

technical field [0001] The present invention relates to complex network technology, and also relates to web data mining technology, specifically a method for discovering complex network communities. Background technique [0002] Network science is an important research direction in an interdisciplinary field, and a prominent and promising research problem in network science is community discovery. In the past, network science mainly focused on the exponential distribution of degree and the small-world effect. The structural description of complex networks has been an important issue of concern to physicists in recent years. From the degree and aggregation coefficient of the individual micro level to the degree distribution and overall aggregation coefficient of the macroscopic overall statistical characteristics. In the middle of these two extremes, there is a description at an intermediate level, which is the community description. Therefore, community discovery has beco...

AI Technical Summary

Problems solved by technology

However, the biggest disadvantage of using the GN algorithm for computing by computers is that the computing speed is slow, it takes up a lot of computer storage space and computer system resources, and it can only handle small and medium-scale network problems, and cannot handle large-scale complex network problems.