Based on the Software Complexity Measurement of Complex Networks under Big Data Technology

Author:

Hong Xiaobin

Abstract

Abstract With the development of the times, computer technology is booming, so the network is becoming more and more complex, software design is becoming more and more complex, because of the protection against a variety of internal or external risks. The internal risk is that the traffic carried by the system is too large to cause the system to crash or the system to crash caused by the code operation error, and the external threat is that hackers use computer technology to break into the system according to security vulnerabilities, so the purpose of this paper is based on big data technology, the software complexity of complex networks is measured and studied. With the consent of the school, we used the school’s internal network data, and after consulting the literature on the complex construction and analysis of complex networks and software, modeled and analyzed it using the improved particle group algorithm. The experimental results show that there is a certain correlation between complex network and software complexity. Because complex networks determine that software requires complex construction to withstand potential risks to keep the software running properly.

Publisher

IOP Publishing

Subject

General Physics and Astronomy

Reference10 articles.

1. A Review of Particle Swarm Optimization[J];Jain;Journal of the Institution of Engineers,2018

2. Extracting core questions in community question answering based on particle swarm optimization[J];Li;Data Technologies and Applications,2019

3. Exploring differential evolution and particle swarm optimization to develop some symmetry-based automatic clustering techniques: application to gene clustering[J];Saha;Neural computing & applications,2018

4. Linear mixed-effects model for longitudinal complex data with diversified characteristics[J];A Z W;Journal of Management Science and Engineering,2020

5. Evaluation of robust outlier detection methods for zero-inflated complex data[J];Templ;Journal of Applied Statistics,2020

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