The research of dynamic community detection is becoming increasingly popular since it can disclose how the community structures change over time in dynamic networks. Evolutionary clustering is often utilized for the goal and has achieved some success, which, however, still has some major drawbacks: (1) The absence of error correction may lead to the result-drifting problem and the error accumulation problem; (2) The NP-hardness of modularity based community detection makes it low efficiency to get an exact solution. In this paper, an efficient and effective multi-objective method, namely DYN-MODPSO, is proposed, and where the traditional evolutionary clustering framework and the particle swarm algorithm are modified and enhanced, respectively. The main contributions include that: (1) A novel strategy, namely the recent future reference, is devised for the initial clustering result correction to make the dynamic community detection more effective; (2) The traditional particle swarm algorithm is improved and integrated with the evolutionary clustering framework by profitably exploiting the proposed strategy; (3) The de-redundant random walk based population initialization is proposed to diversify the individuals in a quality-guaranteed way. Furthermore, the multi-individual crossover operator and the improved interference operator are carefully designed to keep the solution from local optimization. Extensive experiments conducted on the real and the synthetic dynamic networks manifest that the proposed DYN-MODPSO outperforms the competitors in terms of both effectiveness and efficiency.

动态社区检测的研究越来越流行，因为它可以揭示动态网络中社区结构随时间的变化。进化聚类通常被用于该目标并取得了一定的成功，但是仍然存在一些主要的缺点：（1）没有纠错可能会导致结果漂移问题和错误累积问题；（2）基于模块化的社区检测的NP硬度使其获得精确解决方案的效率较低。本文提出了一种有效且有效的多目标方法，即DYN-MODPSO，并对传统的进化聚类框架和粒子群算法分别进行了改进和增强。主要贡献包括：（1）一种新颖的策略，即最近的未来参考，设计用于初始聚类结果校正，以使动态社区检测更加有效；（2）通过有益地利用提出的策略对传统的粒子群算法进行了改进，并与进化聚类框架相集成；（3）提出了基于冗余的随机行走种群初始化，以保证质量的方式使个体多样化。此外，精心设计了多个体交叉算子和改进的干扰算子，以使解决方案不进行局部优化。在真实和合成动态网络上进行的大量实验表明，所提出的DYN-MODPSO在有效性和效率方面都优于竞争对手。