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Community detection is a key factor in analyzing the structural features of complex networks. However, traditional dynamic community detection methods often fail to effectively solve the problems of deep network information loss and computational complexity in hyperbolic space. To address this challenge, a hyperbolic space-based dynamic graph neural network community detection model (HSDCDM) is proposed.

HSDCDM first projects the node features into the hyperbolic space and then utilizes the hyperbolic graph convolution module on the Poincaré and Lorentz models to realize feature fusion and information transfer. In addition, the parallel optimized temporal memory module ensures fast and accurate capture of time domain information over extended periods. Finally, the community clustering module divides the community structure by combining the node characteristics of the space domain and the time domain. To evaluate the performance of HSDCDM, experiments are conducted on both artificial and real datasets.

Experimental results on complex networks demonstrate that HSDCDM significantly enhances the quality of community detection in hierarchical networks. It shows an average improvement of 7.29% in NMI and a 9.07% increase in ARI across datasets compared to traditional methods. For complex networks with non-Euclidean geometric structures, the HSDCDM model incorporating hyperbolic geometry can better handle the discontinuity of the metric space, provides a more compact embedding that preserves the data structure, and offers advantages over methods based on Euclidean geometry methods.

This model aggregates the potential information of nodes in space through manifold-preserving distribution mapping and hyperbolic graph topology modules. Moreover, it optimizes the Simple Recurrent Unit (SRU) on the hyperbolic space Lorentz model to effectively extract time series data in hyperbolic space, thereby enhancing computing efficiency by eliminating the reliance on tangent space.

This study aims to discuss whether the lasting Confucian philosophy could be used in responding to the consequences of technological impacts on jobs in the Age of Robots from a human resource management and development (HRMD) perspective.

Related literature concerning traditional Confucian philosophy and the power of Confucianism was examined. Key perspectives on this topic relating to smart technology were analyzed. Whether Confucian humanity could be used to promote ethical behavior and continuous improvement in the workplace in the Age of Robots was then discussed.

Three propositions were made: humans can better coexist with artificial intelligence (AI) and robots if humanity is valued, cultivated and practiced; some concepts of traditional Confucian philosophy can be applied to support management, employees and organizations to go through the technology-driven social change; and managements and human resource professionals can be the change agent and adopt Confucian paradigm for employees’ and organizational effectiveness in the Age of Robots.

Future research on human–machine interactions and strategic plans to apply Confucian humanity on job restructuring in robotic workplace is recommended.

For organizational development implication, human resource professionals may identify business opportunities, develop human–machine interactions strategic plans, build out creative process and promote moral behaviors and ethical conduct with a growth mindset.

For corporate social responsibility, management and human resource professionals can upskill and reskill employees to develop talents, avoid technology unemployment and advance their human skills to be competitive in the robotic workplace.

This study highlighted how human workers should work like a human, not as a robot, by building a lifelong character through a moral refinement process for self-fulfillment, social responsibility and social stability.