Multi-objective dynamic community detection algorithm for complex networks improved by adaptive mutation strategy

Dynamic network communities evolve over time. Traditional methods suffer from declining partition quality and trajectory discontinuities, compromising the decision accuracy and real-time performance of critical applications such as social recommendation and anomaly detection. Addressing this issue enhances analysis of complex systems' dynamic behaviours. This study proposes a multi-objective dynamic community detection algorithm for complex networks, with an adaptive mutation strategy. It optimizes semantic consistency and topological coherence across time slice communities through dynamic mutation parameter adaptive adjustment and hierarchical event detection. Experimental results demonstrate that the proposed algorithm performs well on both simulated and real dynamic networks: peak community partitioning quality reaches 0.91, the average community cohesion (modularity) reaches 0.87, the average temporal smoothness reaches 0.84, and the peak community event detection rate reaches 94.8%, outperforming baselines; Additionally, the burst response latency is below 90 ms, with significant improvements in computational efficiency and memory usage. The algorithm demonstrates long-term stability (efficiency decay rate <8%), with community overlap detection rates above 0.80. The dynamic community detection platform developed through research can improve the accuracy and real-time processing capability of analyzing complex network evolution patterns, providing efficient and scalable solutions for dynamic decision-making in scenarios such as social recommendation and anomaly monitoring.