Method for partitioning communities in complex dynamic network by virtue of multi-objective local search based on decomposition

Abstract

The invention discloses a method for partitioning communities in a complex dynamic network by virtue of multi-objective local search based on decomposition, and the method is mainly used for solving the problem of poor community partitioning accuracy in the course of processing the complex dynamic network in the prior art. The method is implemented through the following steps: (1) determining objective functions; (2) constructing an initial solution population, and initializing individuals in the solution population by a neighborhood real-number encoding method; (3) sequentially selecting the individuals from the solution population and then carrying out cross variation on the individuals to obtain progeny individuals; (4) updating the solution population by virtue of the progeny individuals; (5) locally searching and updating the solution population; (6) judging whether the population evolution process is terminated: if iterations reach the preset times, executing a step (7), otherwise, transferring to the step (3); and (7) selecting the optimum community partition according to the maximum module density principle. The method disclosed by the invention has the beneficial effects that two objective functions can be optimized at the same time, synchronous analysis of community partition and community evolution is realized, the community partitioning accuracy is improved, and the problem of detection of a community structure in the complex dynamic network can be solved.

Description

technical field [0001] The invention belongs to the field of complex networks, and relates to a dynamic network community division method, in particular to a decomposition-based local search multi-objective complex dynamic network community division method, which can be used to detect community structures in complex dynamic networks. Background technique [0002] Since the end of the 20th century, the rapid development of information technology represented by the internet has brought human society into the network era. In the real world, many systems exist in the form of networks, from the World Wide Web in the Internet world to the route network in the transportation system, from the VLSI in the electronic field to the large-scale power network in the power system, from the cells in the biological system From neural networks to protein interaction networks, from social networks in social relationships to collaborative networks between scientists, complex networks are everyw...

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Problems solved by technology

This method regards the dynamic network community structure detection problem as a two-objective problem, and then uses the non-dominated multi-objective genetic algorithm to optimize the two objective functions simultaneously. The objective function does not need to set the biased parameters and the number of community division modules in advance. However, due to the insufficient local search ability of the genetic algorithm, the community structure obtained when dealing with a dynamic network with high complexity cannot reflect the dynamic network well. Characteristics of the community structure in

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