不依赖于领域知识的重叠社区快速发现算法是当前复杂系统研究的热点.本文基于网络的局部特征,提出描述网络链接疏密程度的关联度,并基于最优节点复制思想,推广到用于描述节点的凝聚程度.提出了采用分割策略的重叠社区发现算法——OCDNOD,通过逐步分割网络,支持独立社区和重叠社区的挖掘.人工网络和实际网络中的实验结果表明算法不仅有较好的时间效率,而且在社区发现的质量方面也优于其它几种代表性的社区发现算法.“,”It is of interest to find field-free overlapping communities with low computation complexity in complex networks.We design a local measure known as the “correlation coefficient” to evaluate the density of links by using node optimized duplication and is extended to assess the agglomeration of nodes.In addition,we propose an overlapping community detection algorithm based on node optimized duplication,which uses a cutting strategy to decompose the network and excavate independent or overlapping communities.Experimental results on synthetic and real-world networks show that the proposed method has a higher accuracy and efficiency than several classical algorithms.