Dynamic community detection based on the Matthew effect

The identification of community structures plays a crucial role in analyzing network topology, exploring network functions, and mining potential patterns in complex networks. Many algorithms have been proposed for identifying community structures in static networks from different perspectives. However, most networks in the real world are not static and their structures constantly evolve over time. Identifying community structures in dynamic networks remains a challenging task because of the variability, complexity, and large scale of dynamic networks. In this study, we propose a framework and Matthew effect model for community detection in dynamic networks. Based on this architecture and model, we design a dynamic community detection algorithm called, Dynamic Community Detection based on the Matthew effect (DCDME), which employs a batch processing method to reveal communities incrementally in each network snapshot. DCDME has several desirable benefits: high-quality community detection, parameter-free operation, and good scalability. Extensive experiments on synthetic and real-world dynamic networks have demonstrated that DCDME has many advantages and outperforms several state-of-the-art algorithms.

Automated summary

This study proposes a framework and Matthew effect model for community detection in dynamic networks, and develops a dynamic community detection algorithm called DCDME, which offers high-quality community detection, parameter-free operation, and good scalability.