Lin Zhu

A mobile communication network is a complex system with millions of interconnected devices, each acting as a node and their communication forming edges. Securing such large-scale networks is a challenging and crucial area of research. While dynamic network structure detection is effective, existing methods often overlook the network topology and local disturbances. Therefore, effectively identifying community structures within the communication network and addressing multimedia security issues remains a significant challenge. This paper presents a dynamic community detection model (DCDAL) based on graph self-coding and a Gaussian mixture model integrated with representation learning to address these issues. The model's performance is evaluated using key network metrics such as NMI and modularization in complex communication networks. Results indicate that the DCDAL model outperforms the comparison model regarding NMI and other indicators, particularly in large-scale mobile communication datasets. The model demonstrates robust performance across multiple evaluation metrics.

The global interest in environmental protection and sustainable development has grown steadily in recent years, sparking widespread concern about green development in the manufacturing industry among governments, enterprises, and scholars around the world. Despite this heightened interest, research on the influence of the digital economy on the manufacturing industry’s green development remains scarce. This article analyzes the impact of the digital economy on the green development of the manufacturing industry and examines the role of human capital and trade competitiveness in this context. Using a two-way fixed-effects model of panel data analysis, we investigated the GTFP (Green Total Factor Productivity) of the Chinese manufacturing industry, as well as that of the overall industry and 20 other Asian countries along the “Belt and Road”. Our findings indicate a positive association between the digital economy and both the overall GTFP and the manufacturing GTFP in China. The countries situated along the Belt and Road experience a U-shaped trend in their GTFP due to the impact of the digital economy. The digital economy displays a delayed impact, and its eco-friendly value is realized, to different extents, after two sequential periods. In addition, human capital and trade competitiveness are threshold variables in the relationship between the digital economy and the green development of the manufacturing industry. When human capital exceeds a certain threshold and trade competitiveness exceeds a certain threshold, the digital economy has a positive effect on GTFP. This study offers unique insights into the impact of the digital economy on the green development of the manufacturing industry. By addressing this research gap, this study gives policymakers the ability to leverage these findings to promote sustainable practices across the industry. Ultimately, the valuable insights provided by this study may contribute to the ongoing efforts to advance the green development of the manufacturing industry.