应对显著变化的动态社区检测方法OA北大核心CSTPCD

In real-world networks,the structure and connections are constantly evolving over time.Detecting community changes within dynamic networks has always been an important research topic.When such changes are significant,it leads to difficulty for community detection algorithms to effectively utilize valuable information from the previous network snapshot,re-sulting in negative transfer in the next time step.To address the issue of poor algorithm adaptability to network mutations,this paper proposed a dynamic community detection algorithm based on genetic evolution ideas and higher-order knowledge transfer strategies.Firstly,it used the adjacency matrix similarity of adjacent snapshots to determine the use of first-order or higher-order information.Then,it employed the spider Web model for population initialization,followed by the non-dominated sorting genetic algorithm NSGA-Ⅱ to iteratively obtain multi-objective optimal solutions on the Pareto front.It designed a novel gene crossover method to enhance population diversity.Finally,experimental results on multiple real and simulated datasets demon-strate that,compared to existing algorithms,the proposed method achieves higher temporal smoothness in community detection results during network upheavals while maintaining a good community modularity level.